Ecosyste.ms: Awesome
An open API service indexing awesome lists of open source software.
https://github.com/monk1337/Awesome-Robust-Machine-Learning
A curated list of Robust Machine Learning papers/articles and recent advancements.
https://github.com/monk1337/Awesome-Robust-Machine-Learning
List: Awesome-Robust-Machine-Learning
awesome-list bias-detection concept-shift covariate-shift data-shift deep-learning distribution-shift fairness fairness-ai fairness-ml fairness-testing federated-learning healthcare healthcare-federated-learning label-shift machine-learning machine-learning-bias ood robust-learning robust-machine-learning
Last synced: 16 days ago
JSON representation
A curated list of Robust Machine Learning papers/articles and recent advancements.
- Host: GitHub
- URL: https://github.com/monk1337/Awesome-Robust-Machine-Learning
- Owner: monk1337
- Created: 2022-10-08T20:13:12.000Z (about 2 years ago)
- Default Branch: main
- Last Pushed: 2022-10-13T04:18:04.000Z (about 2 years ago)
- Last Synced: 2024-05-22T23:03:39.126Z (7 months ago)
- Topics: awesome-list, bias-detection, concept-shift, covariate-shift, data-shift, deep-learning, distribution-shift, fairness, fairness-ai, fairness-ml, fairness-testing, federated-learning, healthcare, healthcare-federated-learning, label-shift, machine-learning, machine-learning-bias, ood, robust-learning, robust-machine-learning
- Homepage:
- Size: 456 KB
- Stars: 18
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- ultimate-awesome - Awesome-Robust-Machine-Learning - A curated list of Robust Machine Learning papers/articles and recent advancements. (Other Lists / Monkey C Lists)
README
# Awesome-Robust-Machine-Learning
Robust machine learning typically refers to the robustness of machine learning algorithms. For a machine learning algorithm to be considered robust, either the testing error has to be consistent with the training error, or the performance is stable after adding some noise to the dataset. This repo contains a curated list of papers/articles and recent advancements in Robust Machine Learning.
[](https://github.com/sindresorhus/awesome)
[](http://makeapullrequest.com)
[](https://opensource.org/licenses/MIT)
##### Table of Contents
1. [Papers](#Robust-Machine-Learning)
- [Robust machine learning](#robust-machine-learning)
- [Robust Machine Learning in Medical Domain](#robust-machine-learning-in-medical-domain)
- [Distribution Shift](#distribution-shift)
- [Distribution shift in question answering](#distribution-shift-in-question-answering)
- [Clinical Distribution Shift](#clinical-distribution-shift)
- [Medical Distribution Shift](#medical-distribution-shift)
2. [Code](#Code)
3. [datasets](#Datasets)
4. [Tutorials](#Tutorials)
5. [Researchers](#Researchers)
## Code
- **Estimating Generalization**
- [[Code]](https://github.com/chingyaoc/estimating-generalization)
- **Adversarial Robustness Toolbox (ART)**
- [[Code]](https://github.com/Trusted-AI/adversarial-robustness-toolbox)
- **Robustness Gym**
- [[Code]](https://github.com/robustness-gym/robustness-gym)
- **WILDS: A benchmark of in-the-wild distribution shifts**
- [[Code]](https://github.com/p-lambda/wilds)
## Tutorials
- **CS282R: Robust Machine Learning Workshop**
- [[Course]](https://github.com/kojino/Harvard-Robust-Machine-Learning)
- **Understand Robustness**
- [[Course]](https://github.com/Nathanlauga/understand-robustness)
## Papers
## Robust machine learning
- **robust machine learning systems: reliability and security for ...**
- [[Paper]](https://ieeexplore.ieee.org/document/8474192)
- **robust machine comprehension models via adversarial ...**
- [[Paper]](https://aclanthology.org/N18-2091)
- **robust distant supervision relation extraction via deep ...**
- [[Paper]](https://aclanthology.org/P18-1199)
- **towards robust neural machine translation - acl anthology**
- [[Paper]](https://aclanthology.org/P18-1163)
- **trainable, scalable summarization using ... - acl anthology**
- [[Paper]](https://aclanthology.org/C98-1009.pdf)
- **learning robust representations of text - acl anthology**
- [[Paper]](https://aclanthology.org/D16-1207.pdf)
- **robust learning, smoothing, and parameter tying on ...**
- [[Paper]](https://aclanthology.org/J95-3002.pdf)
- **robust machine reading comprehension by learning soft ...**
- [[Paper]](https://aclanthology.org/2020.coling-main.248)
- **evaluating neural model robustness for machine ...**
- [[Paper]](https://aclanthology.org/2021.eacl-main.210.pdf)
- **improving robustness of neural machine translation with ...**
- [[Paper]](https://aclanthology.org/W19-5368)
- **learning to reweight examples for robust deep learning**
- [[Paper]](https://proceedings.mlr.press/v80/ren18a/ren18a.pdf)
- **robust learning under uncertain test distributions: relating ...**
- [[Paper]](http://proceedings.mlr.press/v32/wen14.html)
- **robust reinforcement learning: a constrained game ...**
- [[Paper]](https://proceedings.mlr.press/v144/yu21a.html)
- **overfitting in adversarially robust deep learning**
- [[Paper]](http://proceedings.mlr.press/v119/rice20a/rice20a.pdf)
- **robust deep learning as optimal control: insights and ...**
- [[Paper]](http://proceedings.mlr.press/v120/seidman20a/seidman20a.pdf)
- **selfie: refurbishing unclean samples for robust deep ...**
- [[Paper]](https://proceedings.mlr.press/v97/song19b.html)
- **minimax regret optimization for robust machine learning ...**
- [[Paper]](https://proceedings.mlr.press/v178/agarwal22b.html)
- **improving robustness of deep-learning-based image ...**
- [[Paper]](https://proceedings.mlr.press/v119/raj20a.html)
- **efficient and robust automated machine learning**
- **robustness in machine learning - jerry li**
- [[Paper]](https://jerryzli.github.io/robust-ml-fall19.html)
- **robust ml**
- [[Paper]](https://www.robust-ml.org/)
- **robust machine learning models and their applications**
- [[Paper]](https://dspace.mit.edu/handle/1721.1/130760)
- **robust machine learning - data analytics and ... - tum**
- [[Paper]](https://www.cs.cit.tum.de/daml/forschung/robust-machine-learning/)
- **what is the definition of the robustness of a machine learning ...**
- [[Paper]](https://www.researchgate.net/post/What_is_the_definition_of_the_robustness_of_a_machine_learning_algorithm)
- **robust intelligence**
- [[Paper]](https://www.robustintelligence.com/)
- **robust machine learning | the alan turing institute**
- [[Paper]](https://www.turing.ac.uk/research/interest-groups/robust-machine-learning)
- [[Paper]](https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning)
- **robust reinforcement learning**
- [[Paper]](https://papers.nips.cc/paper/1841-robust-reinforcement-learning.pdf)
- **a closer look at accuracy vs. robustness**
- [[Paper]](https://papers.nips.cc/paper/2020/file/61d77652c97ef636343742fc3dcf3ba9-Paper.pdf)
- **provably robust deep learning via adversarially trained ...**
- [[Paper]](https://papers.nips.cc/paper/9307-provably-robust-deep-learning-via-adversarially-trained-smoothed-classifiers.pdf)
- **robust reinforcement learning via adversarial training with ...**
- [[Paper]](https://papers.nips.cc/paper/2020/file/5cb0e249689cd6d8369c4885435a56c2-Paper.pdf)
- **robustness of classifiers: from adversarial to random noise**
- [[Paper]](http://papers.nips.cc/paper/6331-robustness-of-classifiers-from-adversarial-to-random-noise.pdf)
- **boundary thickness and robustness in learning models**
- [[Paper]](https://papers.nips.cc/paper/2020/file/44e76e99b5e194377e955b13fb12f630-Paper.pdf)
- **a causal view on robustness of neural networks**
- [[Paper]](https://papers.nips.cc/paper/2020/file/02ed812220b0705fabb868ddbf17ea20-Paper.pdf)
- **robust reinforcement learning as a stackelberg game via ...**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0430.pdf)
- **global robustness evaluation of deep neural networks with ...**
- [[Paper]](https://www.ijcai.org/proceedings/2019/0824.pdf)
- **improving the robustness of deep neural networks ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2019/403)
- **robust reinforcement learning as a stackelberg ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/430)
- **on guaranteed optimal robust explanations for nlp models**
- [[Paper]](https://www.ijcai.org/proceedings/2021/366)
- **robust learning from noisy side-information by semidefinite ...**
- [[Paper]](https://www.ijcai.org/proceedings/2019/349)
- **towards accurate and robust domain adaptation under ...**
- [[Paper]](https://www.ijcai.org/proceedings/2020/0314.pdf)
- **high-robustness, low-transferability fingerprinting of neural ...**
- [[Paper]](https://www.ijcai.org/proceedings/2021/0080.pdf)
- **on guaranteed optimal robust explanations for nlp ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2021/0366.pdf)
- **towards robust resnet: a small step but a giant leap - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2019/595)
- **learning perturbation sets for robust ma**
- [[Paper]](https://openreview.net/pdf?id=MIDckA56aD)
- **analyzing the robustness of open-world machine learning.**
- [[Paper]](https://openreview.net/forum?id=laa530q5gUR)
- **card: certifiably robust machine learning pipeline via ...**
- [[Paper]](https://openreview.net/forum?id=roaZrQMGsd6&referrer=%5Bthe%20profile%20of%20Bo%20Li%5D(%2Fprofile%3Fid%3D~Bo_Li19))
- **sample selection for fair and robust training - openreview**
- [[Paper]](https://openreview.net/pdf?id=IZNR0RDtGp3)
- **robustness between the worst and average case - nips papers**
- [[Paper]](https://openreview.net/pdf?id=Y8YqrYeFftd)
- **machine learning explainability and robustness - openreview**
- [[Paper]](https://openreview.net/forum?id=REwtJ_Af00Z)
- **secure byzantine-robust machine learning | openreview**
- [[Paper]](https://openreview.net/forum?id=69EFStdgTD2&referrer=%5Bthe%20profile%20of%20Lie%20He%5D(%2Fprofile%3Fid%3D~Lie_He1))
- **robust deep reinforcement learning through adversarial loss**
- [[Paper]](https://openreview.net/forum?id=eaAM_bdW0Q)
- **[2101.02559] robust machine learning systems - arxiv**
- [[Paper]](https://arxiv.org/abs/2101.02559)
- **[2112.00639] robustness in deep learning for computer vision**
- [[Paper]](https://arxiv.org/abs/2112.00639)
- **robust machine learning approach for predicting kinase ...**
- [[Paper]](https://arxiv.org/abs/2111.08008)
- **towards efficient data-centric robust machine learning with ...**
- [[Paper]](https://arxiv.org/abs/2203.03810)
- **why robust generalization in deep learning is difficult - arxiv**
- [[Paper]](http://arxiv.org/abs/2205.13863)
- **rethinking machine learning robustness via its link with the ...**
- [[Paper]](https://arxiv.org/abs/2202.08944)
- **sample-efficient training of robust deep learning models**
- [[Paper]](https://arxiv.org/abs/2112.02542)
- **when doubly robust methods meet machine learning ... - arxiv**
- [[Paper]](https://arxiv.org/abs/2204.10969)
- **[2202.05395] robust, deep, and reinforcement learning for ...**
- [[Paper]](https://arxiv.org/abs/2202.05395)
- **robust learning from observation with model misspecification**
- [[Paper]](https://arxiv.org/abs/2202.06003)
- **neural network for determining an asteroid mineral composition from reflectance spectra**
- [[Paper]](https://arxiv.org/abs/2210.01006)
- **understanding adversarial robustness against on-manifold adversarial examples**
- [[Paper]](https://arxiv.org/abs/2210.00430)
- **learning-based design of luenberger observers for autonomous nonlinear systems**
- [[Paper]](https://arxiv.org/abs/2210.01476)
- **interpretable option discovery using deep q-learning and variational autoencoders**
- [[Paper]](https://arxiv.org/abs/2210.01231)
- **secoe: alleviating sensors failure in machine learning-coupled iot systems**
- [[Paper]](https://arxiv.org/abs/2210.02144)
- **robust fair clustering: a novel fairness attack and defense framework**
- [[Paper]](https://arxiv.org/abs/2210.01953)
- **a closer look at robustness to l-infinity and spatial perturbations and their composition**
- [[Paper]](https://arxiv.org/abs/2210.02577)
- **anomaly detection using data depth: multivariate case**
- [[Paper]](https://arxiv.org/abs/2210.02851)
- **flow matching for generative modeling**
- [[Paper]](https://arxiv.org/abs/2210.02747)
- **subspace learning for feature selection via rank revealing qr factorization: unsupervised and hybrid approaches with non-negative matrix factorization and evolutionary algorithm**
- [[Paper]](https://arxiv.org/abs/2210.00418)
- **evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification**
- [[Paper]](https://arxiv.org/abs/2210.00002)
- **latent state marginalization as a low-cost approach for improving exploration**
- [[Paper]](https://arxiv.org/abs/2210.00999)
- **differentiable parsing and visual grounding of verbal instructions for object placement**
- [[Paper]](https://arxiv.org/abs/2210.00215)
- **random data augmentation based enhancement: a generalized enhancement approach for medical datasets**
- [[Paper]](https://arxiv.org/abs/2210.00824)
- **robust self-healing prediction model for high dimensional data**
- [[Paper]](https://arxiv.org/abs/2210.01788)
- **strength-adaptive adversarial training**
- [[Paper]](https://arxiv.org/abs/2210.01288)
- **feddig: robust federated learning using data digest to represent absent clients**
- [[Paper]](https://arxiv.org/abs/2210.00737)
- **distributionally adaptive meta reinforcement learning**
- [[Paper]](https://arxiv.org/abs/2210.03104)
- **blockchain-based monitoring for poison attack detection in decentralized federated learning**
- [[Paper]](https://arxiv.org/abs/2210.02873)
- **ncvx: a general-purpose optimization solver for constrained machine and deep learning**
- [[Paper]](https://arxiv.org/abs/2210.00973)
- **force-aware interface via electromyography for natural vr/ar interaction**
- [[Paper]](https://arxiv.org/abs/2210.01225)
- **tikhonov regularization is optimal transport robust under martingale constraints**
- [[Paper]](https://arxiv.org/abs/2210.01413)
- **multiguard: provably robust multi-label classification against adversarial examples**
- [[Paper]](https://arxiv.org/abs/2210.01111)
- **unsupervised model selection for time-series anomaly detection**
- [[Paper]](https://arxiv.org/abs/2210.01078)
- **perceptual attacks of no-reference image quality models with human-in-the-loop**
- [[Paper]](https://arxiv.org/abs/2210.00933)
- **robust $q$-learning algorithm for markov decision processes under wasserstein uncertainty**
- [[Paper]](https://arxiv.org/abs/2210.00898)
- **stability via adversarial training of neural network stochastic control of mean-field type**
- [[Paper]](https://arxiv.org/abs/2210.00874)
- **comparison of data representations and machine learning architectures for user identification on arbitrary motion sequences**
- [[Paper]](https://arxiv.org/abs/2210.00527)
- **spectral augmentation for self-supervised learning on graphs**
- [[Paper]](https://arxiv.org/abs/2210.00643)
- **causal estimation for text data with (apparent) overlap violations**
- [[Paper]](https://arxiv.org/abs/2210.00079)
- **codedsi: differentiable code search**
- [[Paper]](https://arxiv.org/abs/2210.00328)
- **solving practical multi-body dynamics problems using a single neural operator**
- [[Paper]](https://arxiv.org/abs/2210.00222)
- **on attacking out-domain uncertainty estimation in deep neural networks**
- [[Paper]](https://arxiv.org/abs/2210.02191)
- **stock volatility prediction using time series and deep learning approach**
- [[Paper]](https://arxiv.org/abs/2210.02126)
- **robust estimation of loss-based measures of model performance under covariate shift**
- [[Paper]](https://arxiv.org/abs/2210.01980)
- **meta-ensemble parameter learning**
- [[Paper]](https://arxiv.org/abs/2210.01973)
- **tree mover's distance: bridging graph metrics and stability of graph neural networks**
- [[Paper]](https://arxiv.org/abs/2210.01906)
- **bayesft: bayesian optimization for fault tolerant neural network architecture**
- [[Paper]](https://arxiv.org/abs/2210.01795)
- **paging with succinct predictions**
- [[Paper]](https://arxiv.org/abs/2210.02775)
- **dynamical systems' based neural networks**
- [[Paper]](https://arxiv.org/abs/2210.02373)
- **practical adversarial attacks on spatiotemporal traffic forecasting models**
- [[Paper]](https://arxiv.org/abs/2210.02447)
- **chemalgebra: algebraic reasoning on chemical reactions**
- [[Paper]](https://arxiv.org/abs/2210.02095)
- **null hypothesis test for anomaly detection**
- [[Paper]](https://arxiv.org/abs/2210.02226)
- **rethinking lipschitz neural networks for certified l-infinity robustness**
- [[Paper]](https://arxiv.org/abs/2210.01787)
- **learning robust kernel ensembles with kernel average pooling**
- [[Paper]](https://arxiv.org/abs/2210.00062)
- **rap: risk-aware prediction for robust planning**
- [[Paper]](https://arxiv.org/abs/2210.01368)
- **adaptive weight decay: on the fly weight decay tuning for improving robustness**
- [[Paper]](https://arxiv.org/abs/2210.00094)
- **hip fracture prediction using the first principal component derived from fea-computed fracture loads**
- [[Paper]](https://arxiv.org/abs/2210.01032)
- **uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles**
- [[Paper]](https://arxiv.org/abs/2210.00336)
- **adversarial robustness of representation learning for knowledge graphs**
- [[Paper]](https://arxiv.org/abs/2210.00122)
- **building robust machine learning systems: current progress, research challenges, and opportunities**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3316781.3323472)
- **building reproducible, reusable, and robust machine learning software**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3401025.3407941)
- **towards lightweight and robust machine learning for cdn caching**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3286062.3286082)
- **a robust machine learning technique to predict low-performing students**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3277569)
- **energy-efficient and adversarially robust machine learning with selective dynamic band filtering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3453688.3461756)
- **robust machine learning-enabled routing for highly mobile vehicular networks with parrot in ns-3**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3460797.3460810)
- **the raise of machine learning hyperparameter constraints in python code**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3533767.3534400)
- **adversarial scrutiny of evidentiary statistical software**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3531146.3533228)
- **continuous lwe**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3406325.3451000)
- **rgrecsys: a toolkit for robustness evaluation of recommender systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3488560.3502192)
- **sentiment analysis using xlm-r transformer and zero-shot transfer learning on resource-poor indian language**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3461764)
- **power-attack: a comprehensive tool-chain for modeling and simulating attacks in power systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3470481.3472705)
- **discovering invariant and changing mechanisms from data**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539479)
- **the road to explainability is paved with bias: measuring the fairness of explanations**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3531146.3533179)
- **predicting cognitive load in an emergency simulation based on behavioral and physiological measures**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340555.3353735)
- **structack: structure-based adversarial attacks on graph neural networks**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3465336.3475110)
- **learning on the rings: self-supervised 3d finger motion tracking using wearable sensors**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534587)
- **ensemble learning for effective run-time hardware-based malware detection: a comprehensive analysis and classification**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3195970.3196047)
- **web-based startup success prediction**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3269206.3272011)
- **robust federated learning based on metrics learning and unsupervised clustering for malicious data detection**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3476883.3520221)
- **when video meets inertial sensors: zero-shot domain adaptation for finger motion analytics with inertial sensors**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3450268.3453537)
- **analyzing hardware based malware detectors**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3061639.3062202)
- **third workshop on adversarial learning methods for machine learning and data mining (advml 2021)**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3469455)
- **the fourth workshop on adversarial learning methods for machine learning and data mining (advml 2022)**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3542897)
- **robust large-scale machine learning in the cloud**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2939672.2939790)
- **adversarial robustness in deep learning: from practices to theories**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3470812)
- **machine learning and data cleaning: which serves the other?**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3506712)
- **a robust mature tomato detection in greenhouse scenes using machine learning and color analysis**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3318299.3318338)
- **privacy risks of securing machine learning models against adversarial examples**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3319535.3354211)
- **fair, robust, and data-efficient machine learning in healthcare**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3514094.3539552)
- **machine learning @ amazon**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2778865.2778867)
- **debiased-cam to mitigate image perturbations with faithful visual explanations of machine learning**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3491102.3517522)
- **robust high dimensional learning for lipschitz and convex losses.**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3455716.3455949)
- **constructivist design for interactive machine learning**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2851581.2892547)
- **machine learning robustness, fairness, and their convergence**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3470799)
- **machine learning explainability and robustness: connected at the hip**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3470806)
- **using machine learning to detect cancer early**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3394486.3409553)
- **security engineering for machine learning (keynote)**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3416507.3428118)
- **trustworthy machine learning: fairness and robustness**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3488560.3502211)
- **hidden stratification causes clinically meaningful failures in machine learning for medical imaging**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3368555.3384468)
- **making machine learning robust against adversarial inputs**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3134599)
- **towards a framework for validating machine learning results in medical imaging: opening the black box**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3332186.3332193)
- **a robust learning approach for regression models based on distributionally robust optimization**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3291125.3291138)
- **set-to-sequence methods in machine learning: a review**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1613/jair.1.12839)
- **machine learning in tourism**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3426826.3426837)
- **incorporating unlabeled data into distributionally-robust learning**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3546258.3546314)
- **attacks and defenses towards machine learning based systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3207677.3277988)
- **appflow: using machine learning to synthesize robust, reusable ui tests**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3236024.3236055)
- **towards robust production machine learning systems: managing dataset shift**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3324884.3415281)
- **secure and robust machine learning for healthcare: a survey**
- [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9153891)
- **learning perturbation sets for robust machine learning**
- [[Paper]](https://arxiv.org/pdf/2007.08450.pdf)
- **robust machine learning systems: challenges,current trends, perspectives, and the road ahead**
- [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8979377)
- **secure byzantine-robust machine learning**
- [[Paper]](https://arxiv.org/pdf/2006.04747.pdf)
- **adversarially robust machine learning with guarantees**
- [[Paper]](https://searchworks.stanford.edu/view/13974182)
- **towards deploying robust machine learning systems**
- [[Paper]](https://doi.org/10.7936/P1G4-5433)
- **a robust machine learning framework for diabetes prediction**
- [[Paper]](https://doi.org/10.1007/978-3-030-89880-9_58)
- **impact-learning: a robust machine learning algorithm**
- [[Paper]](http://dl.acm.org/citation.cfm?id=3411185)
- **regularization helps with mitigating poisoning attacks: distributionally-robust machine learning using the wasserstein distance**
- [[Paper]](https://arxiv.org/pdf/2001.10655.pdf)
- **robust machine learning for colorectal cancer risk prediction and stratification**
- [[Paper]](https://www.ncbi.nlm.nih.gov/pubmed/33693381)
## Robust Machine Learning in Medical Domain
- **robust machine learning variable importance analyses of ...**
- [[Paper]](https://pubmed.ncbi.nlm.nih.gov/29527659/)
- **secure and robust machine learning for healthcare ... - core**
- [[Paper]](https://core.ac.uk/download/pdf/328760438.pdf)
- **rethink robustness of deep learning models for medical ...**
- [[Paper]](https://aimi.stanford.edu/events/research-meeting/aimi-research-meeting-rethink-robustness-deep-learning-models-medical-image)
- **identification of robust deep neural network models of ... - nature**
- [[Paper]](https://www.nature.com/articles/s41746-022-00651-4)
- **robustness of ai-based prognostic and systems health ...**
- [[Paper]](https://www.sciencedirect.com/science/article/abs/pii/S1367578821000195)
- **scalable few-shot learning of robust biomedical name ...**
- [[Paper]](https://aclanthology.org/2021.bionlp-1.3.pdf)
- **applied medical code mapping with character-based deep ...**
- [[Paper]](https://aclanthology.org/2021.naloma-1.2.pdf)
- **enhancing model robustness and fairness with causality**
- [[Paper]](https://aclanthology.org/2021.cinlp-1.3.pdf)
- **identifying patients with pain in emergency departments ...**
- [[Paper]](https://aclanthology.org/U19-1015.pdf)
- **robust machine translation evaluation with entailment ...**
- [[Paper]](https://aclanthology.org/P09-1034.pdf)
- **does robustness improve fairness? approaching fairness ...**
- [[Paper]](https://aclanthology.org/2021.findings-acl.294.pdf)
- **on the lack of robust interpretability of neural text classifiers**
- [[Paper]](https://aclanthology.org/2021.findings-acl.327.pdf)
- **supervised machine learning for extractive query based ...**
- [[Paper]](https://aclanthology.org/W18-5604.pdf)
- **robust benchmarking for machine learning of clinical entity ...**
- [[Paper]](http://proceedings.mlr.press/v126/agrawal20a/agrawal20a.pdf)
- **feature robustness in non-stationary health records:**
- [[Paper]](http://proceedings.mlr.press/v106/nestor19a/nestor19a.pdf)
- **just train twice: improving group robustness without ...**
- [[Paper]](http://proceedings.mlr.press/v139/liu21f/liu21f.pdf)
- **addressing the false negative problem of deep learning ...**
- [[Paper]](https://proceedings.mlr.press/v121/cheng20a.html)
- **what clinicians want: contextualizing explainable machine ...**
- [[Paper]](http://proceedings.mlr.press/v106/tonekaboni19a/tonekaboni19a.pdf)
- **smoothed geometry for robust attribution - nips papers**
- [[Paper]](https://papers.nips.cc/paper/2020/file/9d94c8981a48d12adfeecfe1ae6e0ec1-Paper.pdf)
- **distilling robust and non-robust features in adversarial ...**
- [[Paper]](https://papers.nips.cc/paper/2021/file/8e5e15c4e6d09c8333a17843461041a9-Paper.pdf)
- **exploring architectural ingredients of adversarially robust ...**
- [[Paper]](https://papers.nips.cc/paper/2021/file/2bd7f907b7f5b6bbd91822c0c7b835f6-Paper.pdf)
- **adversarial training helps transfer learning via better ...**
- [[Paper]](https://papers.nips.cc/paper/2021/file/d3aeec875c479e55d1cdeea161842ec6-Paper.pdf)
- **on single source robustness in deep fusion models**
- [[Paper]](https://papers.nips.cc/paper/8728-on-single-source-robustness-in-deep-fusion-models.pdf)
- **coresets for robust training of neural networks against ...**
- [[Paper]](https://papers.nips.cc/paper/2020/file/8493eeaccb772c0878f99d60a0bd2bb3-Paper.pdf)
- **robust classification under sample selection bias**
- [[Paper]](http://papers.nips.cc/paper/5458-robust-classification-under-sample-selection-bias.pdf)
- **metric learning for adversarial robustness**
- [[Paper]](https://papers.nips.cc/paper/8339-metric-learning-for-adversarial-robustness.pdf)
- **robust high-dimensional classification from few positive ...**
- [[Paper]](https://www.ijcai.org/proceedings/2022/271)
- **robust high-dimensional classification from few ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0271.pdf)
- **multimodal attentional neural networks for diagnosis prediction**
- [[Paper]](https://www.ijcai.org/proceedings/2019/0823.pdf)
- **towards adversarially robust deep image denoising - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0211.pdf)
- **hybrid learning system for large-scale medical image analysis**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0824.pdf)
- **ai-powered posture training: application of machine learning ...**
- [[Paper]](https://www.ijcai.org/proceedings/2019/0805.pdf)
- **certified robustness via randomized smoothing over ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0467.pdf)
- **robust interpretable text classification against spurious ...**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0616.pdf)
- **robust and sparse fuzzy k-means clustering - ijcai**
- [[Paper]](https://www.ijcai.org/Proceedings/16/Papers/317.pdf)
- **carben: composite adversarial robustness benchmark**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0851.pdf)
- **robust medical image segmentation by adapting neural ...**
- [[Paper]](https://openreview.net/pdf?id=tv_pkmFzdC)
- **measuring robustness in deep learning based compressive ...**
- [[Paper]](https://openreview.net/pdf?id=HqUeGFCQzX5)
- **conditional synthetic data generation for robust machine ...**
- [[Paper]](https://openreview.net/pdf?id=o4JWdxYTjL8)
- **robust training of recurrent neural networks to handle missing ...**
- [[Paper]](https://openreview.net/forum?id=S1jqMb2oM)
- **reliable and trustworthy machine learning for ... - nips papers**
- [[Paper]](https://openreview.net/pdf?id=hNMOSUxE8o6)
- **robust neural networks are more interpretable for genomics**
- [[Paper]](https://openreview.net/pdf/6b9befb80f2336b2c81716f766c4d4572c2fc827.pdf)
- **robust image segmentation quality assessment - openreview**
- [[Paper]](https://openreview.net/pdf?id=nyhZXiaotm)
- **out of distribution detection and adversarial attacks on deep ...**
- [[Paper]](https://openreview.net/forum?id=1iy7rdPCt_)
- **[2103.08291] robust machine learning in critical care - arxiv**
- [[Paper]](https://arxiv.org/abs/2103.08291)
- **secure and robust machine learning for healthcare ... - arxiv**
- [[Paper]](https://arxiv.org/pdf/2001.08103)
- **robust machine learning in critical care - arxiv**
- [[Paper]](https://arxiv.org/pdf/2103.08291)
- **online reflective learning for robust medical image ... - arxiv**
- [[Paper]](http://arxiv.org/abs/2207.00476)
- **style curriculum learning for robust medical image ... - arxiv**
- [[Paper]](https://arxiv.org/abs/2108.00402)
- **deep learning models are not robust against noise in clinical ...**
- [[Paper]](https://arxiv.org/abs/2108.12242)
- **evaluating the robustness of self-supervised learning in ...**
- [[Paper]](https://arxiv.org/abs/2105.06986)
- **towards to robust and generalized medical image ... - arxiv**
- [[Paper]](https://arxiv.org/abs/2108.03823)
- **uncertainty estimations methods for a deep learning model to aid in clinical decision-making -- a clinician's perspective**
- [[Paper]](https://arxiv.org/abs/2210.00589)
- **feddar: federated domain-aware representation learning**
- [[Paper]](https://arxiv.org/abs/2209.04007)
- **3d ux-net: a large kernel volumetric convnet modernizing hierarchical transformer for medical image segmentation**
- [[Paper]](https://arxiv.org/abs/2209.15076)
- **stacking ensemble learning in deep domain adaptation for ophthalmic image classification**
- [[Paper]](https://arxiv.org/abs/2209.13420)
- **identifying differential equations to predict blood glucose using sparse identification of nonlinear systems**
- [[Paper]](https://arxiv.org/abs/2209.13852)
- **robust and efficient imbalanced positive-unlabeled learning with self-supervision**
- [[Paper]](https://arxiv.org/abs/2209.02459)
- **generalizability of adversarial robustness under distribution shifts**
- [[Paper]](https://arxiv.org/abs/2209.15042)
- **fairness and robustness in anti-causal prediction**
- [[Paper]](https://arxiv.org/abs/2209.09423)
- **feature selection integrated deep learning for ultrahigh dimensional and highly correlated feature space**
- [[Paper]](https://arxiv.org/abs/2209.07011)
- **de-identification of french unstructured clinical notes for machine learning tasks**
- [[Paper]](https://arxiv.org/abs/2209.09631)
- **boxshrink: from bounding boxes to segmentation masks**
- [[Paper]](https://arxiv.org/abs/2208.03142)
- **rrwavenet: a compact end-to-end multi-scale residual cnn for robust ppg respiratory rate estimation**
- [[Paper]](https://arxiv.org/abs/2208.08672)
- **geoecg: data augmentation via wasserstein geodesic perturbation for robust electrocardiogram prediction**
- [[Paper]](https://arxiv.org/abs/2208.01220)
- **adaptive temperature scaling for robust calibration of deep neural networks**
- [[Paper]](https://arxiv.org/abs/2208.00461)
- **rethinking degradation: radiograph super-resolution via aid-srgan**
- [[Paper]](https://arxiv.org/abs/2208.03008)
- **an intertwined neural network model for eeg classification in brain-computer interfaces**
- [[Paper]](https://arxiv.org/abs/2208.08860)
- **machine learning-based eeg applications and markets**
- [[Paper]](https://arxiv.org/abs/2208.05144)
- **bayesian pseudo labels: expectation maximization for robust and efficient semi-supervised segmentation**
- [[Paper]](https://arxiv.org/abs/2208.04435)
- **deformation equivariant cross-modality image synthesis with paired non-aligned training data**
- [[Paper]](https://arxiv.org/abs/2208.12491)
- **federated learning for medical applications: a taxonomy, current trends, challenges, and future research directions**
- [[Paper]](https://arxiv.org/abs/2208.03392)
- **fast-aid brain: fast and accurate segmentation tool using artificial intelligence developed for brain**
- [[Paper]](https://arxiv.org/abs/2208.14360)
- **slice-level detection of intracranial hemorrhage on ct using deep descriptors of adjacent slices**
- [[Paper]](https://arxiv.org/abs/2208.03403)
- **vector-based data improves left-right eye-tracking classifier performance after a covariate distributional shift**
- [[Paper]](https://arxiv.org/abs/2208.00465)
- **learning from imperfect training data using a robust loss function: application to brain image segmentation**
- [[Paper]](https://arxiv.org/abs/2208.04941)
- **predicting microsatellite instability and key biomarkers in colorectal cancer from h&e-stained images: achieving sota predictive performance with fewer data using swin transformer**
- [[Paper]](https://arxiv.org/abs/2208.10495)
- **bpfish: blockchain and privacy-preserving fl inspired smart healthcare**
- [[Paper]](https://arxiv.org/abs/2207.11654)
- **decorrelative network architecture for robust electrocardiogram classification**
- [[Paper]](https://arxiv.org/abs/2207.09031)
- **liver segmentation using turbolift learning for ct and cone-beam c-arm perfusion imaging**
- [[Paper]](https://arxiv.org/abs/2207.10167)
- **online reflective learning for robust medical image segmentation**
- [[Paper]](https://arxiv.org/abs/2207.00476)
- **representation learning with information theory for covid-19 detection**
- [[Paper]](https://arxiv.org/abs/2207.01437)
- **suppressing poisoning attacks on federated learning for medical imaging**
- [[Paper]](https://arxiv.org/abs/2207.10804)
- **advances in prediction of readmission rates using long term short term memory networks on healthcare insurance data**
- [[Paper]](https://arxiv.org/abs/2207.00066)
- **vector quantisation for robust segmentation**
- [[Paper]](https://arxiv.org/abs/2207.01919)
- **pose-based tremor classification for parkinson's disease diagnosis from video**
- [[Paper]](https://arxiv.org/abs/2207.06828)
- **machine learning to predict the antimicrobial activity of cold atmospheric plasma-activated liquids**
- [[Paper]](https://arxiv.org/abs/2207.12478)
- **towards accurate and robust classification in continuously transitioning industrial sprays with mixup**
- [[Paper]](https://arxiv.org/abs/2207.09609)
- **identifying the context shift between test benchmarks and production data**
- [[Paper]](https://arxiv.org/abs/2207.01059)
- **pro-tip: phantom for robust automatic ultrasound calibration by tip detection**
- [[Paper]](https://arxiv.org/abs/2206.05962)
- **a review of causality for learning algorithms in medical image analysis**
- [[Paper]](https://arxiv.org/abs/2206.05498)
- **decentralized distributed learning with privacy-preserving data synthesis**
- [[Paper]](https://arxiv.org/abs/2206.10048)
- **self-supervision on images and text reduces reliance on visual shortcut features**
- [[Paper]](https://arxiv.org/abs/2206.07155)
- **amos: a large-scale abdominal multi-organ benchmark for versatile medical image segmentation**
- [[Paper]](https://arxiv.org/abs/2206.08023)
- **from labels to priors in capsule endoscopy: a prior guided approach for improving generalization with few labels**
- [[Paper]](https://arxiv.org/abs/2206.05288)
- **breast cancer classification using deep learned features boosted with handcrafted features**
- [[Paper]](https://arxiv.org/abs/2206.12815)
- **cass: cross architectural self-supervision for medical image analysis**
- [[Paper]](https://arxiv.org/abs/2206.04170)
- **adaptive adversarial training to improve adversarial robustness of dnns for medical image segmentation and detection**
- [[Paper]](https://arxiv.org/abs/2206.01736)
- **learning underrepresented classes from decentralized partially labeled medical images**
- [[Paper]](https://arxiv.org/abs/2206.15353)
- **independent evaluation of state-of-the-art deep networks for mammography**
- [[Paper]](https://arxiv.org/abs/2206.12407)
- **robust monitoring for medical cyber-physical systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3446913.3460318)
- **detection of nasopharyngeal carcinoma using routine medical tests via machine learning**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3524086.3524102)
- **a machine learning approach for medical device classification**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3494193.3494232)
- **machine learning for the developing world**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3210548)
- **prediction of adverse drug reaction using machine learning and deep learning based on an imbalanced electronic medical records dataset**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3472813.3472817)
- **explainability methods for machine learning systems for multimodal medical datasets: research proposal**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3524273.3533925)
- **analyzing the robustness of open-world machine learning**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3338501.3357372)
- **validation methods to promote real-world applicability of machine learning in medicine**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3441369.3441372)
- **machine learning approaches for extracting genetic medical data information**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3018896.3066906)
- **deep learning for medical anomaly detection – a survey**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3464423)
- **evaluation of applied machine learning for health misinformation detection via survey of medical professionals on controversial topics in pediatrics**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3472813.3472814)
- **robust i/o-compute concurrency for machine learning pipelines in constrained cyber-physical devices**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3461648.3463842)
- **the early detection of subclinical ketosis in dairy cows using machine learning methods**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3055635.3056625)
- **text classification of diseases treated by traditional chinese medicine prescription based on machine learning**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3429889.3429895)
- **deep learning in medical imaging: fmri big data analysis via convolutional neural networks**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3219104.3229250)
- **multi-layer representation learning for medical concepts**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2939672.2939823)
- **a robust framework for accelerated outcome-driven risk factor identification from ehr**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330718)
- **improving early prognosis of dementia using machine learning methods**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3502433)
- **on the need of machine learning as a service for the internet of things**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3109761.3109783)
- **federated multi-view learning for private medical data integration and analysis**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3501816)
- **federated learning in a medical context: a systematic literature review**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3412357)
- **automatic processing of electronic medical records using deep learning**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3240925.3240961)
- **a multi-agent feature selection and hybrid classification model for parkinson's disease diagnosis**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3433180)
- **object detection and classification using machine learning techniques: a comparison of haar cascades and neural networks**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3484824.3484895)
- **automatic differentiation in machine learning: a survey**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3122009.3242010)
- **an adversarial approach for the robust classification of pneumonia from chest radiographs**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3368555.3384458)
- **analysis of machine learning models predicting quality of life for cancer patients**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3444757.3485103)
- **diagnosis of methylmalonic acidemia using machine learning methods**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340997.3341000)
- **assuring the machine learning lifecycle: desiderata, methods, and challenges**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3453444)
- **bringing machine learning closer to non-experts: proposal of a user-friendly machine learning tool in the healthcare domain**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3486011.3486469)
- **robust machine learning in critical care — software engineering and medical perspectives**
- [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9474414)
- **open source robust machine learning software for medical patient data analysis and cloud storage**
- [[Paper]](https://doi.org/10.1007/978-3-030-64610-3_104)
- **robust machine learning variable importance analyses of medical conditions for health care spending**
- [[Paper]](https://doi.org/10.1111/1475-6773.12848)
- **robust machine learning against adversarial samples at test time**
- [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9149002)
- **lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features**
- [[Paper]](https://doi.org/10.3390/app12136517)
- **a robust machine learning predictive model for maternal health risk**
- [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9885515)
- **robust medical image registration and motion modeling based on machine learning. (le recalage robuste d'images médicales et la modélisation du mouvement basée sur l'apprentissage profond)**
- [[Paper]](https://tel.archives-ouvertes.fr/tel-02954033)
- **a robust and stable gene selection algorithm based on graph theory and machine learning**
- [[Paper]](https://doi.org/10.1186/s40246-021-00366-9)
### Distribution Shift
- **difference between distribution shift and data shift, concept ...**
- [[Paper]](https://stats.stackexchange.com/questions/548405/difference-between-distribution-shift-and-data-shift-concept-drift-and-model-dr)
- **understanding dataset shift - towards data science**
- [[Paper]](https://towardsdatascience.com/understanding-dataset-shift-f2a5a262a766)
- **distribution shift framework - deepmind**
- [[Paper]](https://www.deepmind.com/open-source/distribution-shift-framework)
- **4.7. environment and distribution shift**
- [[Paper]](https://d2l.ai/chapter_linear-classification/environment-and-distribution-shift.html)
- **learning to predict and make decisions under distribution shift**
- [[Paper]](https://www.ml.cmu.edu/research/phd-dissertation-pdfs/yw4_phd_ml_2021.pdf)
- **dataset shift in machine learning - mit press**
- [[Paper]](https://mitpress.mit.edu/9780262545877/dataset-shift-in-machine-learning/)
- **mechanical mnist – distribution shift - openbu**
- [[Paper]](https://open.bu.edu/handle/2144/44485)
- **principles of distribution shift (pods) - icml 2022**
- [[Paper]](https://icml.cc/Conferences/2022/ScheduleMultitrack?event=13465)
- **microsoft/distribution-shift-latent-representations - github**
- [[Paper]](https://github.com/microsoft/distribution-shift-latent-representations)
- **neurips distshift workshop 2021 - google sites**
- [[Paper]](https://sites.google.com/view/distshift2021)
- **types of out-of-distribution texts and how to detect them**
- [[Paper]](https://aclanthology.org/2021.emnlp-main.835.pdf)
- **shifted label distribution matters in distantly supervised ...**
- [[Paper]](https://aclanthology.org/D19-1397.pdf)
- **to annotate or not? predicting performance drop under ...**
- [[Paper]](https://aclanthology.org/D19-1222.pdf)
- **contrastive out-of-distribution detection for pretrained ...**
- [[Paper]](https://aclanthology.org/2021.emnlp-main.84.pdf)
- **on continual model refinement in out-of-distribution data ...**
- [[Paper]](https://aclanthology.org/2022.acl-long.223)
- **distributionally robust recurrent decoders with random ...**
- [[Paper]](https://aclanthology.org/2022.repl4nlp-1.1)
- **estimating the impact of domain shift on parser error**
- [[Paper]](https://aclanthology.org/2022.findings-acl.68/)
- **social media text classification under negative covariate shift**
- [[Paper]](https://aclanthology.org/D15-1282.pdf)
- **distributionally robust finetuning bert for covariate drift in ...**
- [[Paper]](https://aclanthology.org/2022.acl-long.139.pdf)
- **adversarial adaptation of synthetic or stale data**
- [[Paper]](https://aclanthology.org/P17-1119)
- **semi-supervised domain adaptation for dependency parsing ...**
- [[Paper]](https://aclanthology.org/2022.acl-long.74)
- **joint and conditional estimation of tagging and parsing models**
- [[Paper]](https://aclanthology.org/P01-1042.pdf)
- **measure and improve robustness in nlp models: a survey**
- [[Paper]](https://aclanthology.org/2022.naacl-main.339.pdf)
- **unlearn dataset bias in natural language inference by fitting ...**
- [[Paper]](https://aclanthology.org/D19-6115)
- **2022.findings-naacl.13.pdf - acl anthology**
- [[Paper]](https://aclanthology.org/2022.findings-naacl.13.pdf)
- **an investigation of the (in)effectiveness of counterfactually ...**
- [[Paper]](https://aclanthology.org/2022.acl-long.256)
- **methods for estimating and improving robustness of ...**
- [[Paper]](https://aclanthology.org/2022.naacl-srw.6.pdf)
- **evaluating lottery tickets under distributional shifts**
- [[Paper]](https://aclanthology.org/D19-6117.pdf)
- **test-time training can close the natural distribution shift ...**
- [[Paper]](https://proceedings.mlr.press/v162/darestani22a.html)
- **examining and combating spurious features under ...**
- [[Paper]](https://proceedings.mlr.press/v139/zhou21g.html)
- **on distribution shift in learning-based bug detectors**
- [[Paper]](https://proceedings.mlr.press/v162/he22a/he22a.pdf)
- **estimating generalization under distribution shifts via domain ...**
- [[Paper]](https://proceedings.mlr.press/v119/chuang20a.html)
- **a label transformation framework for correcting label shift**
- [[Paper]](https://proceedings.mlr.press/v119/guo20d.html)
- **bayesian adaptation for covariate shift**
- [[Paper]](https://papers.nips.cc/paper/2021/hash/07ac7cd13fd0eb1654ccdbd222b81437-Abstract.html)
- **rethinking importance weighting for deep learning under ...**
- [[Paper]](https://papers.nips.cc/paper/2020/hash/8b9e7ab295e87570551db122a04c6f7c-Abstract.html)
- **characterizing generalization under out-of-distribution shifts ...**
- [[Paper]](https://papers.nips.cc/paper/2021/file/d1f255a373a3cef72e03aa9d980c7eca-Paper.pdf)
- **a unified view of label shift estimation - nips papers**
- [[Paper]](https://papers.nips.cc/paper/2020/file/219e052492f4008818b8adb6366c7ed6-Paper.pdf)
- **provably efficient q-learning with function approximation via ...**
- [[Paper]](http://papers.nips.cc/paper/9018-provably-efficient-q-learning-with-function-approximation-via-distribution-shift-error-checking-oracle.pdf)
- **robust federated learning: the case of affine distribution ...**
- [[Paper]](https://papers.nips.cc/paper/2020/file/f5e536083a438cec5b64a4954abc17f1-Paper.pdf)
- **domain adaptation by using causal inference to predict ...**
- [[Paper]](https://papers.nips.cc/paper/8282-domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions.pdf)
- **domain adaptation with conditional distribution matching and ...**
- [[Paper]](https://papers.nips.cc/paper/2020/file/dfbfa7ddcfffeb581f50edcf9a0204bb-Paper.pdf)
- **correcting covariate shift with the frank-wolfe algorithm**
- [[Paper]](https://www.ijcai.org/Proceedings/15/Papers/147.pdf)
- **few-shot adaptation of pre-trained networks for domain shift**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0232.pdf)
- **distance metric learning under covariate shift - ijcai**
- [[Paper]](https://www.ijcai.org/Proceedings/11/Papers/205.pdf)
- **domain generalization through the lens of angular invariance**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0139.pdf)
- **approximate exploitability: learning a best response - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0484.pdf)
- **searching for optimal subword tokenization in cross ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/595)
- **robust domain adaptation: representations, weights and ...**
- [[Paper]](https://www.ijcai.org/proceedings/2021/0644.pdf)
- **domain generalization under conditional and label shifts via ...**
- [[Paper]](https://www.ijcai.org/proceedings/2021/122)
- **identifying instance features for capability-oriented evaluation**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0392.pdf)
- **adversarial bi-regressor network for domain adaptive ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0501.pdf)
- **task-aware lipschitz data augmentation for visual ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0514.pdf)
- **topological uncertainty: monitoring trained neural networks ...**
- [[Paper]](https://www.ijcai.org/proceedings/2021/0367.pdf)
- **test-time fourier style calibration for domain generalization**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0240.pdf)
- **self-managing associative memory for dynamic acquisition of ...**
- [[Paper]](https://www.ijcai.org/Proceedings/09/Papers/169.pdf)
- **learning personalization for cross-silo federated learning**
- [[Paper]](https://www.ijcai.org/proceedings/2022/301)
- **a fine-grained analysis on distribution shift - openreview**
- [[Paper]](https://openreview.net/forum?id=Dl4LetuLdyK)
- **a benchmark of in-the-wild distribution shift over time**
- [[Paper]](https://openreview.net/forum?id=BUQD1tJ2UwK)
- **feature shift detection: localizing which ... - nips papers**
- [[Paper]](https://openreview.net/pdf?id=k1YfNKf0FZn)
- **online adaptation to label distribution shift - openreview**
- [[Paper]](https://openreview.net/forum?id=6h14cMLgb5q)
- **if your data distribution shifts, use self-learning - openreview**
- [[Paper]](https://openreview.net/forum?id=1oEvY1a67c1)
- **how robust are pre-trained models to distribution shift?**
- [[Paper]](https://openreview.net/forum?id=zKDcZBVVEWm)
- **fair predictors under distribution shift - openreview**
- [[Paper]](https://openreview.net/forum?id=zgl6DdYHggo)
- **an empirical study of methods for detecting dataset shift**
- [[Paper]](https://openreview.net/pdf/f9a2ae9ee8021aeb70a8f2deeab247a324b8200e.pdf)
- **robust generalization despite distribution shift via minimum ...**
- [[Paper]](https://openreview.net/attachment?id=kiWRlrbVzSM&name=supplementary_material)
- **tracking the risk of a deployed model - openreview**
- [[Paper]](https://openreview.net/pdf?id=Ro_zAjZppv)
- **towards explaining image-based distribution shifts**
- [[Paper]](https://openreview.net/pdf?id=MpDDnE6_lGC)
- **handling distribution shifts on graphs**
- [[Paper]](https://openreview.net/pdf?id=FQOC5u-1egI)
- **unsupervised attribute alignment for characterizing ...**
- [[Paper]](https://openreview.net/forum?id=Bk1hklAuZyh)
- **reversible instance normalization for ...**
- [[Paper]](https://openreview.net/pdf?id=cGDAkQo1C0p)
- **a fine-grained analysis on distribution shift - arxiv**
- [[Paper]](https://arxiv.org/abs/2110.11328)
- **an empirical study on distribution shift robustness from the ...**
- [[Paper]](https://arxiv.org/abs/2205.12753)
- **maintaining fairness across distribution shift - arxiv**
- [[Paper]](https://arxiv.org/pdf/2202.01034)
- **interpretable distribution shift detection using optimal transport**
- [[Paper]](https://arxiv.org/abs/2208.02896)
- **codes: a distribution shift benchmark dataset for source ...**
- [[Paper]](https://arxiv.org/abs/2206.05480)
- **how robust are pre-trained models to distribution shift? - arxiv**
- [[Paper]](https://arxiv.org/abs/2206.08871)
- **calibrated ensembles can mitigate accuracy tradeoffs under ...**
- [[Paper]](http://arxiv.org/abs/2207.08977)
- **discovering distribution shifts using latent space ... - arxiv**
- [[Paper]](https://arxiv.org/abs/2202.02339)
- **models out of line: a fourier lens on distribution shift ... - arxiv**
- [[Paper]](https://arxiv.org/abs/2207.04075)
- **combating distribution shift for accurate time series ... - arxiv**
- [[Paper]](https://arxiv.org/abs/2202.10808)
- **time series prediction under distribution shift using ... - arxiv**
- [[Paper]](http://arxiv.org/abs/2207.11486)
- **on distribution shift in learning-based bug detectors - arxiv**
- [[Paper]](https://arxiv.org/abs/2204.10049)
- **fairness transferability subject to bounded distribution shift**
- [[Paper]](https://arxiv.org/abs/2206.00129)
- **combating label distribution shift for active domain adaptation**
- [[Paper]](https://arxiv.org/abs/2208.06604)
- **test: test-time self-training under distribution shift - arxiv**
- [[Paper]](https://arxiv.org/abs/2209.11459)
- **[2202.06523] metashift: a dataset of datasets for evaluating ...**
- [[Paper]](https://arxiv.org/abs/2202.06523)
- **estimating test performance for ai medical devices under ...**
- [[Paper]](https://arxiv.org/abs/2207.05796)
- **agreement-on-the-line: predicting the performance of neural ...**
- [[Paper]](https://arxiv.org/abs/2206.13089)
- **contrastive graph few-shot learning**
- [[Paper]](https://arxiv.org/abs/2210.00084)
- **learning an invertible output mapping can mitigate simplicity bias in neural networks**
- [[Paper]](https://arxiv.org/abs/2210.01360)
- **a fixed-point algorithm for the ac power flow problem**
- [[Paper]](https://arxiv.org/abs/2210.01310)
- **percolation properties of the neutron population in nuclear reactors**
- [[Paper]](https://arxiv.org/abs/2210.02413)
- **a review of uncertainty calibration in pretrained object detectors**
- [[Paper]](https://arxiv.org/abs/2210.02935)
- **analysis of irs-assisted downlink wireless networks over generalized fading**
- [[Paper]](https://arxiv.org/abs/2210.02717)
- **exploring effective knowledge transfer for few-shot object detection**
- [[Paper]](https://arxiv.org/abs/2210.02021)
- **gapx: generalized autoregressive paraphrase-identification x**
- [[Paper]](https://arxiv.org/abs/2210.01979)
- **gravitational microlensing by dressed primordial black holes**
- [[Paper]](https://arxiv.org/abs/2210.02078)
- **data drift correction via time-varying importance weight estimator**
- [[Paper]](https://arxiv.org/abs/2210.01422)
- **adawac: adaptively weighted augmentation consistency regularization for volumetric medical image segmentation**
- [[Paper]](https://arxiv.org/abs/2210.01891)
- **on the (un)importance of the transition-dipole phase in the high-harmonic generation from solid state media**
- [[Paper]](https://arxiv.org/abs/2210.02500)
- **satellite-based continuous-variable quantum key distribution under the earth's gravitational field**
- [[Paper]](https://arxiv.org/abs/2210.02776)
- **env-aware anomaly detection: ignore style changes, stay true to content!**
- [[Paper]](https://arxiv.org/abs/2210.03103)
- **spectroscopic localization of atomic sample plane for precise digital holography**
- [[Paper]](https://arxiv.org/abs/2210.02721)
- **benchmarking learnt radio localisation under distribution shift**
- [[Paper]](https://arxiv.org/abs/2210.01930)
- **generalized solution of the paraxial equation**
- [[Paper]](https://arxiv.org/abs/2210.01088)
- **on the effects of data normalisation for domain adaptation on eeg data**
- [[Paper]](https://arxiv.org/abs/2210.01081)
- **spectral-domain method of moments analysis of spatially dispersive graphene patch embedded in planarly layered media**
- [[Paper]](https://arxiv.org/abs/2210.00877)
- **towards performance portable programming for distributed heterogeneous systems**
- [[Paper]](https://arxiv.org/abs/2210.01238)
- **simper: simple self-supervised learning of periodic targets**
- [[Paper]](https://arxiv.org/abs/2210.03115)
- **class-specific channel attention for few-shot learning**
- [[Paper]](https://arxiv.org/abs/2209.01332)
- **deep stable representation learning on electronic health records**
- [[Paper]](https://arxiv.org/abs/2209.01321)
- **a demonstration of over-the-air computation for federated edge learning**
- [[Paper]](https://arxiv.org/abs/2209.09954)
- **s2p: state-conditioned image synthesis for data augmentation in offline reinforcement learning**
- [[Paper]](https://arxiv.org/abs/2209.15256)
- **domain generalization -- a causal perspective**
- [[Paper]](https://arxiv.org/abs/2209.15177)
- **a domain adaptive deep learning solution for scanpath prediction of paintings**
- [[Paper]](https://arxiv.org/abs/2209.11338)
- **robust bayesian non-segmental detection of multiple change-points**
- [[Paper]](https://arxiv.org/abs/2209.14995)
- **modelling the energy distribution in chime/frb catalog-1**
- [[Paper]](https://arxiv.org/abs/2209.12961)
- **exact and efficient multivariate two-sample tests through generalized linear rank statistics**
- [[Paper]](https://arxiv.org/abs/2209.14235)
- **black-box audits for group distribution shifts**
- [[Paper]](https://arxiv.org/abs/2209.03620)
- **semi-supervised triply robust inductive transfer learning**
- [[Paper]](https://arxiv.org/abs/2209.04977)
- **polito-iit-cini submission to the epic-kitchens-100 unsupervised domain adaptation challenge for action recognition**
- [[Paper]](https://arxiv.org/abs/2209.04525)
- **non-parametric temporal adaptation for social media topic classification**
- [[Paper]](https://arxiv.org/abs/2209.05706)
- **test-time adaptation with principal component analysis**
- [[Paper]](https://arxiv.org/abs/2209.05779)
- **giant overreflection of magnetohydrodynamic waves from inhomogeneous plasmas with nonuniform shear flows**
- [[Paper]](https://arxiv.org/abs/2209.08061)
- **mitigating both covariate and conditional shift for domain generalization**
- [[Paper]](https://arxiv.org/abs/2209.08253)
- **visible-infrared person re-identification using privileged intermediate information**
- [[Paper]](https://arxiv.org/abs/2209.09348)
- **improving replay-based continual semantic segmentation with smart data selection**
- [[Paper]](https://arxiv.org/abs/2209.09839)
- **towards optimization and model selection for domain generalization: a mixup-guided solution**
- [[Paper]](https://arxiv.org/abs/2209.00652)
- **future gradient descent for adapting the temporal shifting data distribution in online recommendation systems**
- [[Paper]](https://arxiv.org/abs/2209.01143)
- **meta-learning with less forgetting on large-scale non-stationary task distributions**
- [[Paper]](https://arxiv.org/abs/2209.01501)
- **robustness and invariance properties of image classifiers**
- [[Paper]](https://arxiv.org/abs/2209.02408)
- **importance tempering: group robustness for overparameterized models**
- [[Paper]](https://arxiv.org/abs/2209.08745)
- **exploiting instance-based mixed sampling via auxiliary source domain supervision for domain-adaptive action detection**
- [[Paper]](https://arxiv.org/abs/2209.15439)
- **too fine or too coarse? the goldilocks composition of data complexity for robust left-right eye-tracking classifiers**
- [[Paper]](https://arxiv.org/abs/2209.03761)
- **record: resource constrained semi-supervised learning under distribution shift**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3394486.3403214)
- **towards reliable multimodal stress detection under distribution shift**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3461615.3486570)
- **confidence may cheat: self-training on graph neural networks under distribution shift**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3485447.3512172)
- **active model adaptation under unknown shift**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539262)
- **balance-subsampled stable prediction across unknown test data**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477052)
- **focused context balancing for robust offline policy evaluation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330852)
- **an empirical study on data distribution-aware test selection for deep learning enhancement**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3511598)
- **a critical reassessment of the saerens-latinne-decaestecker algorithm for posterior probability adjustment**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3433164)
- **understanding the effect of out-of-distribution examples and interactive explanations on human-ai decision making**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3479552)
- **co-training disentangled domain adaptation network for leveraging popularity bias in recommenders**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531952)
- **causpref: causal preference learning for out-of-distribution recommendation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3485447.3511969)
- **off-policy actor-critic for recommender systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3523227.3546758)
- **hybridrepair: towards annotation-efficient repair for deep learning models**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3533767.3534408)
- **decoupled reinforcement learning to stabilise intrinsically-motivated exploration**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3535850.3535978)
- **neural statistics for click-through rate prediction**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531762)
- **influence function for unbiased recommendation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401321)
- **dcaf-bert: a distilled cachable adaptable factorized model for improved ads ctr prediction**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3524206)
- **making adversarially-trained language models forget with model retraining: a case study on hate speech detection**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3524667)
- **causal discovery from heterogeneous/nonstationary data**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3455716.3455805)
- **svem: a signal variation elimination model for eeg emotion recognition**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3543081.3543085)
- **adarnn: adaptive learning and forecasting of time series**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3459637.3482315)
- **transfer string kernel for cross-context dna-protein binding prediction**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1109/TCBB.2016.2609918)
- **trustworthy graph learning: reliability, explainability, and privacy protection**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3542597)
- **real negatives matter: continuous training with real negatives for delayed feedback modeling**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3467086)
- **a new generation of perspective api: efficient multilingual character-level transformers**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539147)
- **robust self-supervised structural graph neural network for social network prediction**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3485447.3512182)
- **stable prediction across unknown environments**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3219819.3220082)
- **learning security classifiers with verified global robustness properties**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3460120.3484776)
- **fairness violations and mitigation under covariate shift**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3442188.3445865)
- **quantifying the performance of adversarial training on language models with distribution shifts**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3494108.3522764)
- **fedrs: federated learning with restricted softmax for label distribution non-iid data**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3467254)
- **case study on distribution strategy through biclustering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3387263.3387271)
- **semi-supervised flexible joint distribution adaptation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3375998.3376022)
- **analysis of the modified weibull distribution for estimation of wind speed distribution**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2832987.2833059)
- **l-shift: encoding and shifting material properties and functionalities with phase-shifting liquid**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2677199.2688807)
- **embedded out-of-distribution detection on an autonomous robot platform**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3445034.3460509)
- **distribution-matching embedding for visual domain adaptation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/2946645.3007061)
- **transfer learning with dynamic distribution adaptation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3360309)
- **probabilistic modeling for frequency vectors using a flexible shifted-scaled dirichlet distribution prior**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3406242)
- **monitoring perception reliability in autonomous driving: distributional shift detection for estimating the impact of input data on prediction accuracy**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3488904.3493382)
- **dynamical origins of distribution functions**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330842)
- **ignis: scaling distribution-oblivious systems with light-touch distribution**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3314221.3314586)
- **control of networked traffic flow distribution: a stochastic distribution system perspective**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3109761.3158411)
- **modeling neurocognitive reaction time with gamma distribution**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3167918.3167941)
- **a feedback shift correction in predicting conversion rates under delayed feedback**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3366423.3380032)
- **a crowdsourcing approach for the inference of distribution grids**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3208903.3208927)
- **adapting covariate shift for legal ai**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3322640.3326720)
- **semi-supervised drifted stream learning with short lookback**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539297)
- **on the theory of policy gradient methods: optimality, approximation, and distribution shift**
- [[Paper]](http://jmlr.org/papers/v22/19-736.html)
- **test-time adaptation to distribution shift by confidence maximization and input transformation**
- [[Paper]](https://arxiv.org/pdf/2106.14999.pdf)
- **mandoline: model evaluation under distribution shift**
- [[Paper]](https://arxiv.org/pdf/2107.00643.pdf)
- **examining and combating spurious features under distribution shift**
- [[Paper]](https://arxiv.org/pdf/2106.07171.pdf)
- **stable prediction with model misspecification and agnostic distribution shift**
- [[Paper]](https://arxiv.org/pdf/2001.11713.pdf)
- **rethinking importance weighting for deep learning under distribution shift**
- [[Paper]](https://arxiv.org/pdf/2006.04662.pdf)
- **online adaptation to label distribution shift**
- [[Paper]](https://arxiv.org/pdf/2107.04520.pdf)
- **near-optimal linear regression under distribution shift**
- [[Paper]](https://arxiv.org/pdf/2106.12108.pdf)
- **closing the closed-loop distribution shift in safe imitation learning**
- [[Paper]](no_link_found)
### Distribution shift in question answering
- **the effect of natural distribution shift on question answering models ...**
- [[Paper]](https://dl.acm.org/doi/abs/10.5555/3524938.3525579)
- **the effect of natural distribution shift on ... - researchgate**
- [[Paper]](https://www.researchgate.net/publication/341069459_The_Effect_of_Natural_Distribution_Shift_on_Question_Answering_Models)
- **the effect of natural distribution shift on ... - karl krauth**
- [[Paper]](https://karlk.net/publication/dist_shift/)
- **out-of-domain question answering - stanford university**
- [[Paper]](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1214/reports/final_reports/report163.pdf)
- **selective question answering under domain shift**
- [[Paper]](https://aclanthology.org/2020.acl-main.503.pdf)
- **topic transferable table question answering - acl anthology**
- [[Paper]](https://aclanthology.org/2021.emnlp-main.342.pdf)
- **re-evaluating conversational question answering**
- [[Paper]](https://aclanthology.org/2022.acl-long.555.pdf)
- **improving calibration in question answering - acl anthology**
- [[Paper]](https://aclanthology.org/2021.findings-acl.172.pdf)
- **improving question answering model robustness with ...**
- [[Paper]](https://aclanthology.org/2021.emnlp-main.696.pdf)
- **contrastive domain adaptation for question answering using ...**
- [[Paper]](https://aclanthology.org/2021.emnlp-main.754.pdf)
- **on the efficacy of adversarial data collection for question ...**
- [[Paper]](https://aclanthology.org/2021.acl-long.517.pdf)
- **entity-based knowledge conflicts in question answering**
- [[Paper]](https://aclanthology.org/2021.emnlp-main.565.pdf)
- **improving unsupervised question answering via ...**
- [[Paper]](https://aclanthology.org/2021.emnlp-main.340.pdf)
- **challenges in generalization in open domain question ...**
- [[Paper]](https://aclanthology.org/2022.findings-naacl.155.pdf)
- **accuracy on the line: on the strong correlation between out ...**
- [[Paper]](https://proceedings.mlr.press/v139/miller21b/miller21b.pdf)
- **wilds: a benchmark of in-the-wild distribution shifts**
- [[Paper]](http://proceedings.mlr.press/v139/koh21a/koh21a.pdf)
- **instabilities of offline rl with pre-trained neural ...**
- [[Paper]](http://proceedings.mlr.press/v139/wang21z/wang21z.pdf)
- **data determines distributional robustness in contrastive ...**
- [[Paper]](https://proceedings.mlr.press/v162/fang22a/fang22a.pdf)
- **lyapunov density models: constraining distribution shift in ...**
- [[Paper]](https://proceedings.mlr.press/v162/kang22a/kang22a.pdf)
- **active learning under label shift**
- [[Paper]](http://proceedings.mlr.press/v130/zhao21b/zhao21b.pdf)
- **predicting out-of-distribution error with the projection norm**
- [[Paper]](https://proceedings.mlr.press/v162/yu22i/yu22i.pdf)
- **can autonomous vehicles identify, recover from,and adapt ...**
- [[Paper]](http://proceedings.mlr.press/v119/filos20a/filos20a.pdf)
- **measuring robustness to natural distribution shifts in image ...**
- [[Paper]](https://papers.nips.cc/paper/2020/file/d8330f857a17c53d217014ee776bfd50-Paper.pdf)
- **adaptive conformal inference under distribution shift**
- [[Paper]](https://papers.nips.cc/paper/2021/file/0d441de75945e5acbc865406fc9a2559-Paper.pdf)
- **overparameterization improves robustness to covariate shift ...**
- [[Paper]](https://papers.nips.cc/paper/2021/file/73fed7fd472e502d8908794430511f4d-Paper.pdf)
- **revisiting the calibration of modern neural networks**
- [[Paper]](https://papers.nips.cc/paper/2021/file/8420d359404024567b5aefda1231af24-Paper.pdf)
- **reducing unimodal biases for visual question answering**
- [[Paper]](https://papers.nips.cc/paper/8371-rubi-reducing-unimodal-biases-for-visual-question-answering.pdf)
- **debiased visual question answering from feature and ...**
- [[Paper]](https://papers.nips.cc/paper/2021/file/1f4477bad7af3616c1f933a02bfabe4e-Paper.pdf)
- **on the value of out-of-distribution testing: an example of ...**
- [[Paper]](https://papers.nips.cc/paper/2020/file/045117b0e0a11a242b9765e79cbf113f-Paper.pdf)
- **mitigating covariate shift in imitation learning via offline data ...**
- [[Paper]](https://papers.nips.cc/paper/2021/file/07d5938693cc3903b261e1a3844590ed-Paper.pdf)
- **react: out-of-distribution detection with rectified activations**
- [[Paper]](https://papers.nips.cc/paper/2021/file/01894d6f048493d2cacde3c579c315a3-Paper.pdf)
- **learning imbalanced datasets with label-distribution-aware ...**
- [[Paper]](https://papers.nips.cc/paper/8435-learning-imbalanced-datasets-with-label-distribution-aware-margin-loss.pdf)
- **ai foundations for human visual perception driven cognitive ...**
- [[Paper]](http://www.ijcai.org/Proceedings/16/Papers/374.pdf)
- **k-best: a new method for real-time decision making - ijcai**
- [[Paper]](https://www.ijcai.org/Proceedings/95-1/Papers/030.pdf)
- **provable guarantees on the robustness of decision rules to ...**
- [[Paper]](https://www.ijcai.org/proceedings/2021/0585.pdf)
- **robustifying vision transformer without retraining from ...**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0141.pdf)
- **exploiting the sign of the advantage function to learn ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2019/0625.pdf)
- **bridging causality and learning: how do they benefit from ...**
- [[Paper]](https://www.ijcai.org/proceedings/2020/0725.pdf)
- **towards robust dense retrieval via local ranking alignment**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0275.pdf)
- **robustness guarantees for credal bayesian networks ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0677.pdf)
- **multi-agent concentrative coordination with decentralized ...**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0085.pdf)
- **session no. 13 computer understanding ii (representation)**
- [[Paper]](http://www.ijcai.org/Proceedings/71/Papers/048.pdf)
- **an information-theoretic approach to distribution shifts**
- [[Paper]](https://openreview.net/forum?id=GrZmKDYCp6H)
- **a closer look at distribution shifts and out-of ... - openreview**
- [[Paper]](https://openreview.net/forum?id=2JFVnWuvrvV)
- **a fine-grained analysis on distribution shift**
- [[Paper]](https://openreview.net/pdf?id=Dl4LetuLdyK)
- **a dataset of real distributional shift across multiple large ...**
- [[Paper]](https://openreview.net/forum?id=qM45LHaWM6E)
- **addressing distribution shift in offline-to ...**
- [[Paper]](https://openreview.net/pdf?id=9hgEG-k57Zj)
- **anoshift: a distribution shift benchmark for unsupervised ...**
- [[Paper]](https://openreview.net/forum?id=rbrouCKPiej)
- **addressing distribution shift in online reinforcement ...**
- [[Paper]](https://openreview.net/forum?id=9hgEG-k57Zj)
- **detecting and adapting to irregular distribution shifts in ...**
- [[Paper]](https://openreview.net/forum?id=-440wKL2oJV)
- **generative question answering: learning to ...**
- [[Paper]](https://openreview.net/pdf?id=Bkx0RjA9tX)
- **if your data distribution shifts, use self-learning**
- [[Paper]](https://openreview.net/pdf?id=1oEvY1a67c1)
- **the effect of natural distribution shift on question answering ...**
- [[Paper]](https://arxiv.org/abs/2004.14444)
- **improving out-of-distribution robustness via selective ... - arxiv**
- [[Paper]](https://arxiv.org/pdf/2201.00299)
- **toward a fine-grained analysis of distribution shifts in ... - arxiv**
- [[Paper]](https://arxiv.org/pdf/2205.02870)
- **arxiv:2006.05121v3 [cs.cv] 7 apr 2021**
- [[Paper]](https://arxiv.org/pdf/2006.05121)
- **afine-grained analysis on distribution shift - arxiv**
- [[Paper]](https://arxiv.org/pdf/2110.11328)
- **arxiv:2207.08739v1 [cs.cv] 18 jul 2022**
- [[Paper]](https://arxiv.org/pdf/2207.08739)
- **x-ggm: graph generative modeling for out-of-distribution ...**
- [[Paper]](https://arxiv.org/pdf/2107.11576)
- **arxiv:2207.01168v1 [cs.lg] 4 jul 2022**
- [[Paper]](https://arxiv.org/pdf/2207.01168)
- **how good are deep models in understanding\\ the generated ...**
- [[Paper]](https://arxiv.org/abs/2208.10760)
- **task formulation matters when learning continually: a case study in visual question answering**
- [[Paper]](https://arxiv.org/abs/2210.00044)
- **complexity-based prompting for multi-step reasoning**
- [[Paper]](https://arxiv.org/abs/2210.00720)
- **test-time adaptation for visual document understanding**
- [[Paper]](https://arxiv.org/abs/2206.07240)
- **data determines distributional robustness in contrastive language image pre-training (clip)**
- [[Paper]](https://arxiv.org/abs/2205.01397)
- **rethinking evaluation practices in visual question answering: a case study on out-of-distribution generalization**
- [[Paper]](https://arxiv.org/abs/2205.12191)
- **teaching models to express their uncertainty in words**
- [[Paper]](https://arxiv.org/abs/2205.14334)
- **improving passage retrieval with zero-shot question generation**
- [[Paper]](https://arxiv.org/abs/2204.07496)
- **data-suite: data-centric identification of in-distribution incongruous examples**
- [[Paper]](https://arxiv.org/abs/2202.08836)
- **on a class of lacunary almost newman polynomials modulo p and density theorems**
- [[Paper]](https://arxiv.org/abs/2201.03233)
- **question generation for evaluating cross-dataset shifts in multi-modal grounding**
- [[Paper]](https://arxiv.org/abs/2201.09639)
- **local distributional chaos**
- [[Paper]](https://arxiv.org/abs/2112.01457)
- **causal forecasting:generalization bounds for autoregressive models**
- [[Paper]](https://arxiv.org/abs/2111.09831)
- **dair: data augmented invariant regularization**
- [[Paper]](https://arxiv.org/abs/2110.11205)
- **topic transferable table question answering**
- [[Paper]](https://arxiv.org/abs/2109.07377)
- **aggregate or not? exploring where to privatize in dnn based federated learning under different non-iid scenes**
- [[Paper]](https://arxiv.org/abs/2107.11954)
- **pointer value retrieval: a new benchmark for understanding the limits of neural network generalization**
- [[Paper]](https://arxiv.org/abs/2107.12580)
- **invariance principle meets information bottleneck for out-of-distribution generalization**
- [[Paper]](https://arxiv.org/abs/2106.06607)
- **a winning hand: compressing deep networks can improve out-of-distribution robustness**
- [[Paper]](https://arxiv.org/abs/2106.09129)
- **approximate bayesian computation for an explicit-duration hidden markov model of covid-19 hospital trajectories**
- [[Paper]](https://arxiv.org/abs/2105.00773)
- **crossnorm and selfnorm for generalization under distribution shifts**
- [[Paper]](https://arxiv.org/abs/2102.02811)
- **a new tool for precise mapping of local temperature fields in submicrometer aqueous volumes**
- [[Paper]](https://arxiv.org/abs/2102.07461)
- **leveraging uncertainty from deep learning for trustworthy materials discovery workflows**
- [[Paper]](https://arxiv.org/abs/2012.01478)
- **coping with label shift via distributionally robust optimisation**
- [[Paper]](https://arxiv.org/abs/2010.12230)
- **some factors of nonsingular bernoulli shifts**
- [[Paper]](https://arxiv.org/abs/2010.04636)
- **a tale of two cities: software developers working from home during the covid-19 pandemic**
- [[Paper]](https://arxiv.org/abs/2008.11147)
- **roses are red, violets are blue... but should vqa expect them to?**
- [[Paper]](https://arxiv.org/abs/2006.05121)
- **generalized mean shift with triangular kernel profile**
- [[Paper]](https://arxiv.org/abs/2001.02165)
- **metaci: meta-learning for causal inference in a heterogeneous population**
- [[Paper]](https://arxiv.org/abs/1912.03960)
- **stationary distributions for the voter model in $d\geq 3$ are factors of iid**
- [[Paper]](https://arxiv.org/abs/1908.09450)
- **dark matter haloes and subhaloes**
- [[Paper]](https://arxiv.org/abs/1907.11775)
- **information-theoretic considerations in batch reinforcement learning**
- [[Paper]](https://arxiv.org/abs/1905.00360)
- **biobert: a pre-trained biomedical language representation model for biomedical text mining**
- [[Paper]](https://arxiv.org/abs/1901.08746)
- **maximum of branching brownian motion in a periodic environment**
- [[Paper]](https://arxiv.org/abs/1812.04189)
- **density estimation for shift-invariant multidimensional distributions**
- [[Paper]](https://arxiv.org/abs/1811.03744)
- **thick points of random walk and the gaussian free field**
- [[Paper]](https://arxiv.org/abs/1809.04369)
- **koala: a new paradigm for election coverage**
- [[Paper]](https://arxiv.org/abs/1807.09665)
- **accounting for phenology in the analysis of animal movement**
- [[Paper]](https://arxiv.org/abs/1806.09473)
- **when can multi-site datasets be pooled for regression? hypothesis tests, $\ell_2$-consistency and neuroscience applications**
- [[Paper]](https://arxiv.org/abs/1709.00640)
- **distributed strong diameter network decomposition**
- [[Paper]](https://arxiv.org/abs/1602.05437)
- **achieving delay rate-function optimality in ofdm downlink with time-correlated channels**
- [[Paper]](https://arxiv.org/abs/1601.06241)
- **lyman alpha emitting galaxies in the nearby universe**
- [[Paper]](https://arxiv.org/abs/1505.07483)
- **experimental round-robin differential phase-shift quantum key distribution**
- [[Paper]](https://arxiv.org/abs/1505.08142)
- **constraints on planet occurrence around nearby mid-to-late m dwarfs from the mearth project**
- [[Paper]](https://arxiv.org/abs/1307.3178)
- **x-ggm: graph generative modeling for out-of-distribution generalization in visual question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3474085.3475350)
- **question rewriting for conversational question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3437963.3441748)
- **passage similarity and diversification in non-factoid question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3471158.3472249)
- **conversational question answering over passages by leveraging word proximity networks**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401399)
- **adapting visual question answering models for enhancing multimodal community q&a platforms**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3357384.3358000)
- **select, substitute, search: a new benchmark for knowledge-augmented visual question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3404835.3463259)
- **sanitizing synthetic training data generation for question answering over knowledge graphs**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3409256.3409836)
- **naranjo question answering using end-to-end multi-task learning model**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330770)
- **meaningful answer generation of e-commerce question-answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3432689)
- **attentive history selection for conversational question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3357384.3357905)
- **answer interaction in non-factoid question answering systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3295750.3298946)
- **product-aware answer generation in e-commerce question-answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3289600.3290992)
- **a discourse-based approach for arabic question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2988238)
- **verification of the expected answer type for biomedical question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3184558.3191542)
- **spatiotemporal-textual co-attention network for video question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3320061)
- **quantifying human-perceived answer utility in non-factoid question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3406522.3446028)
- **answer ranking based on named entity types for question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3022227.3022297)
- **adversarial learning of answer-related representation for visual question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3269206.3271765)
- **video question answering via knowledge-based progressive spatial-temporal attention network**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3321505)
- **opinion-aware answer generation for review-driven question answering in e-commerce**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340531.3411904)
- **xalgo: a design probe of explaining algorithms’ internal states via question-answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397481.3450676)
- **scaling up online question answering via similar question retrieval**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2876034.2893428)
- **cross-domain knowledge distillation for retrieval-based question answering systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3442381.3449814)
- **distributed deep learning for question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2983323.2983377)
- **a non-factoid question-answering taxonomy**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531926)
- **social question answering: textual, user, and network features for best answer prediction**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2948063)
- **automatically extracting high-quality negative examples for answer selection in question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3077136.3080645)
- **dynamic graph reasoning for conversational open-domain question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3498557)
- **conversational question answering on heterogeneous sources**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531815)
- **open-retrieval conversational question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401110)
- **fast parameter adaptation for few-shot image captioning and visual question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3240508.3240527)
- **a corpus for hybrid question answering systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3184558.3191540)
- **explainable conversational question answering over heterogeneous sources**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531688)
- **temporal question answering in news article collections**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3526023)
- **asking for help in community question-answering: the goal-framing effect of question expression on response networks**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3529372.3533287)
- **complex temporal question answering on knowledge graphs**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3459637.3482416)
- **qanswer: towards question answering search over websites**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3524250)
- **semantic question answering on big data**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2928294.2928302)
- **a chinese knowledge base question answering system**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3459637.3481970)
- **knowledge graph embedding based question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3289600.3290956)
- **duplicate detection in programming question answering communities**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3169795)
- **a factoid question answering system for vietnamese**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3184558.3191535)
- **humor detection in product question answering systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401077)
- **exploring diversification in non-factoid question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3234944.3234973)
- **designing a question-answering system for comic contents**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3011549.3011554)
- **performance prediction for non-factoid question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3341981.3344249)
- **non-factoid question answering in the legal domain**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3331184.3331431)
- **natural language question answering in the financial domain**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3291291.3291311)
- **more accurate question answering on freebase**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2806416.2806472)
- **table cell search for question answering**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2872427.2883080)
- **the effect of natural distribution shift on question answering models**
- [[Paper]](https://arxiv.org/pdf/2004.14444.pdf)
- **a survey of causality in visual question answering**
- [[Paper]](https://gaoxiangluo.github.io/files/A_Survey_of_Causality_in_Visual_Question_Answering.pdf)
- **learning neural models for natural language processing in the face of distributional shift**
- [[Paper]](https://arxiv.org/pdf/2109.01558.pdf)
- **empirical or invariant risk minimization? a sample complexity perspective**
- [[Paper]](https://arxiv.org/pdf/2010.16412.pdf)
- **l g ] 9 n ov 2 01 8 density estimation for shift-invariant multidimensional distributions**
- [[Paper]](http://export.arxiv.org/pdf/1811.03744)
- **grit: general robust image task benchmark**
- [[Paper]](https://arxiv.org/pdf/2204.13653.pdf)
- **qagan: adversarial approach to learning domain invariant language features**
- [[Paper]](https://arxiv.org/pdf/2206.12388.pdf)
- **active learning over multiple domains in natural language tasks**
- [[Paper]](https://arxiv.org/pdf/2202.00254.pdf)
- **a sample complexity perspective**
- [[Paper]](https://openreview.net/pdf?id=jrA5GAccy_)
- **how good are deep models in understanding the generated images?**
- [[Paper]](https://arxiv.org/pdf/2208.10760.pdf)
### Clinical Distribution Shift
- **data distribution shifts and monitoring - chip huyen**
- [[Paper]](https://huyenchip.com/2022/02/07/data-distribution-shifts-and-monitoring.html)
- **shifting the distribution | evidence for population health**
- [[Paper]](https://academic.oup.com/book/9251/chapter/155945428)
- **principles for tackling distribution shift - youtube**
- [[Paper]](https://www.youtube.com/watch?v=QKBh6TmvBaw)
- **zachary c. lipton: deep learning under distribution shift**
- [[Paper]](https://www.youtube.com/watch?v=WhpZKIra-FQ)
- **preventing dataset shift from breaking machine-learning ...**
- [[Paper]](https://hal.archives-ouvertes.fr/hal-03293375/file/main.pdf)
- **adapting event extractors to medical data - acl anthology**
- [[Paper]](https://aclanthology.org/2021.eacl-main.258.pdf)
- **distinguishing clinical sentiment: the importance of domain ...**
- [[Paper]](https://aclanthology.org/W19-1915.pdf)
- **the performance differences of a medical code prediction ...**
- [[Paper]](https://aclanthology.org/2022.clinicalnlp-1.10.pdf)
- **investigating the challenges of temporal relation extraction ...**
- [[Paper]](https://aclanthology.org/W18-5607.pdf)
- **rethinking group-robust algorithms in a label-wise setting**
- [[Paper]](https://aclanthology.org/2022.findings-acl.192.pdf)
- **analyzing dynamic adversarial training data in the limit**
- [[Paper]](https://aclanthology.org/2022.findings-acl.18.pdf)
- **how to leverage the multimodal ehr data for better medical ...**
- [[Paper]](https://aclanthology.org/2021.emnlp-main.329.pdf)
- **gcn with external knowledge for clinical event detection**
- [[Paper]](https://aclanthology.org/2021.ccl-1.106.pdf)
- **examining dataset shift during prospective validation**
- [[Paper]](https://proceedings.mlr.press/v149/otles21a/otles21a.pdf)
- **evaluating domain generalization for survival analysis in ...**
- [[Paper]](https://proceedings.mlr.press/v174/pfisterer22a/pfisterer22a.pdf)
- **understanding clinical collaborations through federated ...**
- [[Paper]](https://proceedings.mlr.press/v149/caldas21a/caldas21a.pdf)
- **domain adaptation under target and conditional shift**
- [[Paper]](http://proceedings.mlr.press/v28/zhang13d.pdf)
- **robust causal inference under covariate shift via worst-case ...**
- [[Paper]](http://proceedings.mlr.press/v125/jeong20a/jeong20a.pdf)
- **what went wrong and when ... - review for neurips paper**
- [[Paper]](https://papers.nips.cc/paper/2020/file/08fa43588c2571ade19bc0fa5936e028-Review.html)
- **what went wrong and when? instance-wise feature ...**
- [[Paper]](https://papers.nips.cc/paper/2020/hash/08fa43588c2571ade19bc0fa5936e028-Abstract.html)
- **evaluating model performance under worst-case ...**
- [[Paper]](https://papers.nips.cc/paper/2021/file/908075ea2c025c335f4865f7db427062-Paper.pdf)
- **from predictions to decisions: using l kahead regularization**
- [[Paper]](https://papers.nips.cc/paper/2020/file/2adcfc3929e7c03fac3100d3ad51da26-Paper.pdf)
- **domain generalization via model-agnostic learning of ...**
- [[Paper]](https://papers.nips.cc/paper/8873-domain-generalization-via-model-agnostic-learning-of-semantic-features.pdf)
- **improving robustness against common corruptions by ...**
- [[Paper]](https://papers.nips.cc/paper/2020/file/85690f81aadc1749175c187784afc9ee-Paper.pdf)
- **likelihood ratios for out-of-distribution detection - openreview**
- [[Paper]](https://papers.nips.cc/paper/9611-likelihood-ratios-for-out-of-distribution-detection.pdf)
- **the "moving targets" training algorithm**
- [[Paper]](http://papers.nips.cc/paper/233-the-moving-targets-training-algorithm.pdf)
- **self-supervised adversarial distribution regularization for ...**
- [[Paper]](https://www.ijcai.org/proceedings/2021/0431.pdf)
- **modeling physicians' utterances to explore diagnostic ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2017/0517.pdf)
- **unsupervised domain adaptation with dual-scheme fusion ...**
- [[Paper]](https://www.ijcai.org/proceedings/2020/0455.pdf)
- **unsupervised cross-modality domain adaptation of ... - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2018/0096.pdf)
- **metric learning in optimal transport for domain adaptation**
- [[Paper]](https://www.ijcai.org/proceedings/2020/0299.pdf)
- **truly batch apprenticeship learning with deep successor ...**
- [[Paper]](https://www.ijcai.org/proceedings/2019/0819.pdf)
- **collaborative filtering on ordinal user feedback - ijcai**
- [[Paper]](https://www.ijcai.org/Proceedings/13/Papers/449.pdf)
- **prototypes and production rulest an approach ...**
- [[Paper]](https://www.ijcai.org/Proceedings/79-1/Papers/004.pdf)
- **learning sparse interpretable features for nas scoring ...**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0220.pdf)
- **building text classifiers with minimal supervision - ijcai**
- [[Paper]](http://www.ijcai.org/Proceedings/11/Papers/208.pdf)
- **reliable and trustworthy machine learning for health using ...**
- [[Paper]](https://openreview.net/forum?id=hNMOSUxE8o6)
- **a benchmark of in-the-wild distribution shift over time - openreview**
- [[Paper]](https://openreview.net/pdf?id=F9ENmZABB0)
- **hidden in plain sight: subgroup shifts escape ood detection**
- [[Paper]](https://openreview.net/forum?id=aZgiUNye2Cz)
- **neurips 2021 workshop distshift - openreview**
- [[Paper]](https://openreview.net/group?id=NeurIPS.cc/2021/Workshop/DistShift)
- **beds-bench: behavior of ehr-models under distributional ...**
- [[Paper]](https://openreview.net/pdf?id=IKWYt4w1uDp)
- **continual domain incremental learning for chest x-ray ...**
- [[Paper]](https://openreview.net/forum?id=tjUEd5OsxuA)
- **a benchmark for text quantification learning under ... - openreview**
- [[Paper]](https://openreview.net/pdf?id=MndqjaCwQX)
- **metashift:adataset of datasets for evaluat**
- [[Paper]](https://openreview.net/pdf?id=MTex8qKavoS)
- **medshift: identifying shift data for medical dataset curation**
- [[Paper]](https://arxiv.org/pdf/2112.13885)
- **test-time adaptation with calibration of medical image ... - arxiv**
- [[Paper]](http://arxiv.org/abs/2207.00769)
- **arxiv:2207.05796v1 [cs.lg] 12 jul 2022**
- [[Paper]](https://arxiv.org/pdf/2207.05796)
- **distribution shift in airline customer behavior during covid-19**
- [[Paper]](https://arxiv.org/abs/2111.14938)
- **understanding behavior of clinical models under domain shifts**
- [[Paper]](https://arxiv.org/pdf/1809.07806)
- **arxiv:2206.15274v1 [eess.iv] 30 jun 2022**
- [[Paper]](https://arxiv.org/pdf/2206.15274)
- **arxiv:2207.00769v2 [eess.iv] 9 jul 2022**
- [[Paper]](https://arxiv.org/pdf/2207.00769)
- **robust and efficient medical imaging with self-supervision**
- [[Paper]](https://arxiv.org/pdf/2205.09723)
- **feather-light fourier domain adaptation in magnetic resonance imaging**
- [[Paper]](https://arxiv.org/abs/2208.00474)
- **optimal transport features for morphometric population analysis**
- [[Paper]](https://arxiv.org/abs/2208.05891)
- **distance-based detection of out-of-distribution silent failures for covid-19 lung lesion segmentation**
- [[Paper]](https://arxiv.org/abs/2208.03217)
- **task-agnostic continual hippocampus segmentation for smooth population shifts**
- [[Paper]](https://arxiv.org/abs/2208.03206)
- **continual learning for tumor classification in histopathology images**
- [[Paper]](https://arxiv.org/abs/2208.03609)
- **generalizable and robust deep learning algorithm for atrial fibrillation diagnosis across ethnicities, ages and sexes**
- [[Paper]](https://arxiv.org/abs/2207.09667)
- **domain-adaptive 3d medical image synthesis: an efficient unsupervised approach**
- [[Paper]](https://arxiv.org/abs/2207.00844)
- **risk-sensitive reinforcement learning: iterated cvar and the worst path**
- [[Paper]](https://arxiv.org/abs/2206.02678)
- **three applications of conformal prediction for rating breast density in mammography**
- [[Paper]](https://arxiv.org/abs/2206.12008)
- **exposing and addressing the fragility of neural networks in digital pathology**
- [[Paper]](https://arxiv.org/abs/2206.15274)
- **maxstyle: adversarial style composition for robust medical image segmentation**
- [[Paper]](https://arxiv.org/abs/2206.01737)
- **test-time adaptation with shape moments for image segmentation**
- [[Paper]](https://arxiv.org/abs/2205.07983)
- **uncertainty-informed deep learning models enable high-confidence predictions for digital histopathology**
- [[Paper]](https://arxiv.org/abs/2204.04516)
- **on-the-fly test-time adaptation for medical image segmentation**
- [[Paper]](https://arxiv.org/abs/2203.05574)
- **chexstray: real-time multi-modal data concordance for drift detection in medical imaging ai**
- [[Paper]](https://arxiv.org/abs/2202.02833)
- **a field of experts prior for adapting neural networks at test time**
- [[Paper]](https://arxiv.org/abs/2202.05271)
- **a privacy-preserving unsupervised domain adaptation framework for clinical text analysis**
- [[Paper]](https://arxiv.org/abs/2201.07317)
- **robust scatterer number density segmentation of ultrasound images**
- [[Paper]](https://arxiv.org/abs/2201.06143)
- **bayesian logistic regression for online recalibration and revision of risk prediction models with performance guarantees**
- [[Paper]](https://arxiv.org/abs/2110.06866)
- **quantifying the reproducibility of graph neural networks using multigraph brain data**
- [[Paper]](https://arxiv.org/abs/2109.02248)
- **memory-aware curriculum federated learning for breast cancer classification**
- [[Paper]](https://arxiv.org/abs/2107.02504)
- **anatomy of domain shift impact on u-net layers in mri segmentation**
- [[Paper]](https://arxiv.org/abs/2107.04914)
- **temporal dependencies in feature importance for time series predictions**
- [[Paper]](https://arxiv.org/abs/2107.14317)
- **intra- and inter-fraction relative range verification in heavy-ion therapy using filtered interaction vertex imaging**
- [[Paper]](https://arxiv.org/abs/2106.09090)
- **cross-modality brain tumor segmentation via bidirectional global-to-local unsupervised domain adaptation**
- [[Paper]](https://arxiv.org/abs/2105.07715)
- **more generalizable models for sepsis detection under covariate shift**
- [[Paper]](https://arxiv.org/abs/2105.09385)
- **out-of-distribution detection in dermatology using input perturbation and subset scanning**
- [[Paper]](https://arxiv.org/abs/2105.11160)
- **feddg: federated domain generalization on medical image segmentation via episodic learning in continuous frequency space**
- [[Paper]](https://arxiv.org/abs/2103.06030)
- **multi-institutional collaborations for improving deep learning-based magnetic resonance image reconstruction using federated learning**
- [[Paper]](https://arxiv.org/abs/2103.02148)
- **spectral decoupling allows training transferable neural networks in medical imaging**
- [[Paper]](https://arxiv.org/abs/2103.17171)
- **therapeutics data commons: machine learning datasets and tasks for drug discovery and development**
- [[Paper]](https://arxiv.org/abs/2102.09548)
- **chexternal: generalization of deep learning models for chest x-ray interpretation to photos of chest x-rays and external clinical settings**
- [[Paper]](https://arxiv.org/abs/2102.08660)
- **computer-aided abnormality detection in chest radiographs in a clinical setting via domain-adaptation**
- [[Paper]](https://arxiv.org/abs/2012.10564)
- **deeplesionbrain: towards a broader deep-learning generalization for multiple sclerosis lesion segmentation**
- [[Paper]](https://arxiv.org/abs/2012.07950)
- **federated semi-supervised learning for covid region segmentation in chest ct using multi-national data from china, italy, japan**
- [[Paper]](https://arxiv.org/abs/2011.11750)
- **transducer adaptive ultrasound volume reconstruction**
- [[Paper]](https://arxiv.org/abs/2011.08419)
- **multi-coil mri reconstruction challenge -- assessing brain mri reconstruction models and their generalizability to varying coil configurations**
- [[Paper]](https://arxiv.org/abs/2011.07952)
- **chasing your long tails: differentially private prediction in health care settings**
- [[Paper]](https://arxiv.org/abs/2010.06667)
- **contrastive cross-site learning with redesigned net for covid-19 ct classification**
- [[Paper]](https://arxiv.org/abs/2009.07652)
- **"name that manufacturer". relating image acquisition bias with task complexity when training deep learning models: experiments on head ct**
- [[Paper]](https://arxiv.org/abs/2008.08525)
- **adapting event extractors to medical data: bridging the covariate shift**
- [[Paper]](https://arxiv.org/abs/2008.09266)
- **speed-of-sound imaging by differential phase contrast with angular compounding**
- [[Paper]](https://arxiv.org/abs/2007.03156)
- **domain adaptation for ultrasound beamforming**
- [[Paper]](https://arxiv.org/abs/2007.03096)
- **shape-aware meta-learning for generalizing prostate mri segmentation to unseen domains**
- [[Paper]](https://arxiv.org/abs/2007.02035)
- **probabilistic self-learning framework for low-dose ct denoising**
- [[Paper]](https://arxiv.org/abs/2006.00327)
- **mask: a flexible framework to facilitate de-identification of clinical texts**
- [[Paper]](https://arxiv.org/abs/2005.11687)
- **risk projection for time-to-event outcome leveraging summary statistics with source individual-level data**
- [[Paper]](https://arxiv.org/abs/2003.01850)
- **balancing confidentiality and sharing of genomic and phenotypic data in a clinical research system**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3233547.3233641)
- **clinical and non-clinical handovers: designing for critical moments**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2998181.2998333)
- **scaling up hci research: from clinical trials to deployment in the wild.**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3411763.3443437)
- **research on automatic proofreading method of clinical terminology of traditional chinese medicine**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3358695.3360943)
- **huge cohorts, genomics, and clinical data to personalize medicine**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3233547.3233608)
- **the objective structured clinical examination (osce) in high-fidelity simulations for assessing nursing students' clinical judgment**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3345120.3345127)
- **the objective structured clinical examination for assessing nursing student clinical competency in a high-fidelity simulation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3416797.3416826)
- **cohort-based clinical trial retrieval**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3503516.3503529)
- **text mining in clinical domain: dealing with noise**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2939672.2939720)
- **aggregating semantic information nuggets for answering clinical queries**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2851613.2851694)
- **on multi-armed bandit designs for dose-finding clinical trials**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3546258.3546272)
- **optimizing clinical spatial resources with iot**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3373017.3373047)
- **explaining machine learning models for clinical gait analysis**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3474121)
- **temporal relation extraction in clinical texts: a systematic review**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3462475)
- **ontology-aware clinical abstractive summarization**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3331184.3331319)
- **automatic extraction of nested entities in clinical referrals in spanish**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3498324)
- **learning to rate clinical concepts using simulated clinician feedback**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3025171.3025232)
- **science2cure: a clinical trial search prototype**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3404835.3462797)
- **“brilliant ai doctor” in rural clinics: challenges in ai-powered clinical decision support system deployment**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3411764.3445432)
- **leveraging the cloud for intelligent clinical data registries**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3307339.3343464)
- **attention-based clinical note summarization**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477314.3507256)
- **conflict discovery and analysis for clinical trials**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3079452.3079494)
- **modeling clinical data from publications**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/2873003.2873011)
- **robustness evaluation of computer-aided clinical trials for medical devices**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3302509.3311058)
- **the evolutionary development of a serious game for clinical laboratory students**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3306500.3306545)
- **multi-disciplinary fairness considerations in machine learning for clinical trials**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3531146.3533154)
- **protecting interoperable clinical environment with authentication**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3076125.3076129)
- **analysis of differences in clinical index between lung cancer patients with or without metastasis**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3107514.3107529)
- **methodology for learning and acquiring clinical skills through simulation with artificial human models**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3486011.3486461)
- **exploiting social media to enhance clinical decision support**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3358695.3360899)
- **fast learning-based registration of sparse 3d clinical images**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3368555.3384462)
- **a frequency-filtering strategy of obtaining phi-free sentences from clinical data repository**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2808719.2808752)
- **clinical and genomics data integration using meta-dimensional approach**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2996890.3007896)
- **application analysis of artificial intelligence target controlled infusion in clinical anesthesia operation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3495018.3495389)
- **clinical decision support system based on knn/ontology extraction method**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3432291.3432305)
- **driving time-dependent paths in clinical bpmn processes**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3019612.3019620)
- **medical question answering for clinical decision support**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2983323.2983819)
- **network analysis and recommendation for infectious disease clinical trial research**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3307339.3342156)
- **a test collection for matching patients to clinical trials**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2911451.2914672)
- **agile clinical decision support development and implementation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3233547.3233663)
- **sonic therapy for anxiety management in clinical settings**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3154862.3154892)
- **a framework to design successful clinical decision support systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3056540.3064960)
- **dependable integrated clinical system architecture with runtime verification**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3199700.3199831)
- **automated encoding of clinical guidelines into computer-interpretable format**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3194480.3194495)
- **leveraging word embeddings and semantic enrichment for automatic clinical evidence grading**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3194480.3194492)
- **exploring the cultivation mode of critical thinking in clinical-thinking training of diagnostics**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3535756.3535776)
- **more text please! understanding and supporting the use of visualization for clinical text overview**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3173574.3173996)
- **estimating test performance for ai medical devices under distribution shift with conformal prediction**
- [[Paper]](https://arxiv.org/pdf/2207.05796.pdf)
- **ehr foundation models improve robustness in the presence of temporal distribution shift**
- [[Paper]](https://doi.org/10.1101/2022.04.15.22273900)
- **study of changing trend in the clinical distribution of candida species in various clinical samples at tertiary care hospital, ahmedabad, gujarat -**
- [[Paper]](http://www.njcmindia.org/home/download/993)
- **sood: self-supervised out-of-distribution detection under domain shift for multi-class colorectal cancer tissue types**
- [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9607789)
- **shift in translations: data work with patient-generated health data in clinical practice**
- [[Paper]](https://doi.org/10.1177/1460458219833097)
- **evaluating model robustness and stability to dataset shift**
- [[Paper]](http://proceedings.mlr.press/v130/subbaswamy21a.html)
- **a new look into uveitis in colombia: changes in distribution patterns and clinical characteristics over the last 25 years**
- [[Paper]](https://doi.org/10.1007/s00417-022-05796-2)
- **muscle pain induces a shift of the spatial distribution of upper trapezius muscle activity during a repetitive task: a mechanism for perpetuation of pain with repetitive activity?**
- [[Paper]](https://doi.org/10.1097/AJP.0000000000000513)
- **robust multivariate nonparametric tests for detection of two-sample location shift in clinical trials**
- [[Paper]](https://pdfs.semanticscholar.org/b5f0/c684be516a4f5801270c0d185b0bef9a0785.pdf)
- **an empirical framework for domain generalization in clinical settings**
- [[Paper]](http://dl.acm.org/citation.cfm?id=3451878)
### Medical Distribution Shift
- **shift distribution – shiftdistribution.com**
- [[Paper]](http://www.shiftdistribution.com/)
- **ehr foundation models improve robustness in the presence ...**
- [[Paper]](https://www.medrxiv.org/content/10.1101/2022.04.15.22273900v1.full)
- **maintaining fairness across distribution shift: do we have ...**
- [[Paper]](https://montrealethics.ai/maintaining-fairness-across-distribution-shift-do-we-have-viable-solutions-for-real-world-applications/)
- **characterizing the value of information in medical notes**
- [[Paper]](https://aclanthology.org/2020.findings-emnlp.187)
- **assessing group-level gender bias in professional evaluations**
- [[Paper]](https://aclanthology.org/2022.gebnlp-1.11.pdf)
- **ziad obermeyer - acl anthology**
- [[Paper]](https://aclanthology.org/people/z/ziad-obermeyer/)
- **preventing failures due to dataset shift: learning predictive ...**
- [[Paper]](http://proceedings.mlr.press/v89/subbaswamy19a/subbaswamy19a.pdf)
- **enhancing unsupervised domain adaptation via semantic ...**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0426.pdf)
- **forecasting patient outcomes in kidney exchange - ijcai**
- [[Paper]](https://www.ijcai.org/proceedings/2022/0701.pdf)
- **distributed patient scheduling in hospitals - ijcai**
- [[Paper]](https://www.ijcai.org/Proceedings/03/Papers/175.pdf)
- **a fine-grained analysis of distribution shifts - openreview**
- [[Paper]](https://openreview.net/pdf?id=AVTfiZgV64X)
- **[2110.14019] reliable and trustworthy machine learning for health ...**
- [[Paper]](https://ar5iv.labs.arxiv.org/html/2110.14019)
- **arxiv:1910.00199v3 [cs.cv] 10 feb 2021**
- [[Paper]](https://arxiv.org/pdf/1910.00199)
- **frequency dropout: feature-level regularization via randomized filtering**
- [[Paper]](https://arxiv.org/abs/2209.09844)
- **q-net: query-informed few-shot medical image segmentation**
- [[Paper]](https://arxiv.org/abs/2208.11451)
- **fusion: fully unsupervised test-time stain adaptation via fused normalization statistics**
- [[Paper]](https://arxiv.org/abs/2208.14206)
- **domain adaptation under open set label shift**
- [[Paper]](https://arxiv.org/abs/2207.13048)
- **revisiting inlier and outlier specification for improved out-of-distribution detection**
- [[Paper]](https://arxiv.org/abs/2207.05286)
- **unsupervised domain adaptation using feature disentanglement and gcns for medical image classification**
- [[Paper]](https://arxiv.org/abs/2206.13123)
- **single-domain generalization in medical image segmentation via test-time adaptation from shape dictionary**
- [[Paper]](https://arxiv.org/abs/2206.14467)
- **distributional gaussian processes layers for out-of-distribution detection**
- [[Paper]](https://arxiv.org/abs/2206.13346)
- **certifying some distributional fairness with subpopulation decomposition**
- [[Paper]](https://arxiv.org/abs/2205.15494)
- **fedilc: weighted geometric mean and invariant gradient covariance for federated learning on non-iid data**
- [[Paper]](https://arxiv.org/abs/2205.09305)
- **dltta: dynamic learning rate for test-time adaptation on cross-domain medical images**
- [[Paper]](https://arxiv.org/abs/2205.13723)
- **fraug: tackling federated learning with non-iid features via representation augmentation**
- [[Paper]](https://arxiv.org/abs/2205.14900)
- **cd$^2$-pfed: cyclic distillation-guided channel decoupling for model personalization in federated learning**
- [[Paper]](https://arxiv.org/abs/2204.03880)
- **direct mapping from pet coincidence data to proton-dose and positron activity using a deep learning approach**
- [[Paper]](https://arxiv.org/abs/2204.13905)
- **rood-mri: benchmarking the robustness of deep learning segmentation models to out-of-distribution and corrupted data in mri**
- [[Paper]](https://arxiv.org/abs/2203.06060)
- **efficient fully distributed federated learning with adaptive local links**
- [[Paper]](https://arxiv.org/abs/2203.12281)
- **larmor frequency shift from magnetized cylinders with arbitrary orientation distribution**
- [[Paper]](https://arxiv.org/abs/2203.01191)
- **biomechanical lower limb model to predict patellar position alteration after medial open wedge high tibial osteotomy**
- [[Paper]](https://arxiv.org/abs/2203.16903)
- **unsupervised domain adaptation for cross-modality retinal vessel segmentation via disentangling representation style transfer and collaborative consistency learning**
- [[Paper]](https://arxiv.org/abs/2201.04812)
- **comparison of measurement systems for assessing number- and mass-based particle filtration efficiency**
- [[Paper]](https://arxiv.org/abs/2201.12446)
- **multi-domain balanced sampling improves out-of-distribution generalization of chest x-ray pathology prediction models**
- [[Paper]](https://arxiv.org/abs/2112.13734)
- **specificity-preserving federated learning for mr image reconstruction**
- [[Paper]](https://arxiv.org/abs/2112.05752)
- **mitigating domain shift in ai-based tuberculosis screening with unsupervised domain adaptation**
- [[Paper]](https://arxiv.org/abs/2111.04893)
- **reliable and trustworthy machine learning for health using dataset shift detection**
- [[Paper]](https://arxiv.org/abs/2110.14019)
- **natural attribute-based shift detection**
- [[Paper]](https://arxiv.org/abs/2110.09276)
- **dispensed transformer network for unsupervised domain adaptation**
- [[Paper]](https://arxiv.org/abs/2110.14944)
- **adapt to adaptation: learning personalization for cross-silo federated learning**
- [[Paper]](https://arxiv.org/abs/2110.08394)
- **distributionally robust multi-output regression ranking**
- [[Paper]](https://arxiv.org/abs/2109.12803)
- **unsupervised domain adaptation in semantic segmentation based on pixel alignment and self-training**
- [[Paper]](https://arxiv.org/abs/2109.14219)
- **the impact of domain shift on left and right ventricle segmentation in short axis cardiac mr images**
- [[Paper]](https://arxiv.org/abs/2109.13230)
- **a study of the multi-agent benefit distribution model in the transformation of medical science and technology achievements**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340037.3340038)
- **research of selection of distribution in the tasks of the regional system of medical prevention**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3388984.3390873)
- **comparative study of mutualisation scenarios for distribution of non-medical products**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3286606.3286828)
- **medical specialists retrieval system using unified medical language system**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3107514.3107521)
- **distribution scheduling model of multiple temperature refrigerated container system**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3317614.3317628)
- **phosphorylated physarum polycephalum 14-3-3 modulates the distribution of the p. polycephalum sr-like protein through the arginine/serine-rich domain: p14-3-3 modulates the distribution of psr**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3460238.3460261)
- **energy exchange model in routed energy distribution network**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3360322.3361017)
- **a study on the impact of shopping value on loyalty due to the activation of omni-channel based on mobile application by distribution companies**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3317614.3317636)
- **a phase shifting multiple filter design methodology for lucy-richardson deconvolution of log-mixtures complex rtn tail distribution**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2800986.2800996)
- **efficient signature scheme using extended chaotic maps for medical imaging records**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3418094.3418144)
- **maximum domain of attraction of the conditional exponential-weibull distribution**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3274250.3274256)
- **medical nutrition therapy for adult patients receiving extracorporeal membrane oxygenation**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3472813.3472829)
- **optimization of contract distribution based on multi-objective estimation of distribution algorithm**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3194452.3194455)
- **open routed energy distribution network based on a concept of energy router in smart grid**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3366030.3366036)
- **distribution big data technology of active distribution network based on edge computing**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3501409.3501637)
- **kernel distribution embeddings: universal kernels, characteristic kernels and kernel metrics on distributions**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3291125.3309606)
- **fisher consistency for prior probability shift**
- [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3122009.3176839)
- **distribution-level markets under high renewable energy penetration**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3538637.3538846)
- **an estimation of distribution algorithm based on the natural gradient and the boltzmann distribution**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2739480.2754803)
- **research on university takeaway o2o distribution mode based on centralized distribution of third-party sub-region**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3404649.3404651)
- **niching an estimation-of-distribution algorithm by hierarchical gaussian mixture learning**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3071178.3071283)
- **the line loss calculation method of active distribution network based on equivalent capacity method**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3331453.3361314)
- **simulation application for improving the efficiency of new distribution centers**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1109/DS-RT.2015.20)
- **application and effect analysis of series reactive power compensation in low voltage distribution network**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3429536.3429537)
- **supporting the self-care practices of shift workers**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3282894.3282914)
- **towards flexible wireless charging for medical implants using distributed antenna system**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3372224.3380899)
- **multiobjective discrete differential evolution for service restoration in energy distribution systems**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2908961.2931642)
- **medical modeling and numerical analysis of thoracoabdominal aortic aneurysm**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340037.3340048)
- **tough shift: exploring the complexities of shifting residential electricity use through a casual mobile game**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2793107.2793108)
- **accessibility analysis of hospitals medical services in urban modernization**
- [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3418094.3418101)
- **test-time adaptation with calibration of medical image classification nets for label distribution shift**
- [[Paper]](https://arxiv.org/pdf/2207.00769.pdf)
- **performance deterioration of deep neural networks for lesion classification in mammography due to distribution shift: an analysis based on artificially created distribution shift**
- [[Paper]](https://doi.org/10.1117/12.2551346)
- **how reliable are out-of-distribution generalization methods for medical image segmentation?**
- [[Paper]](https://arxiv.org/pdf/2109.01668.pdf)
- **the domain shift problem of medical image segmentation and vendor-adaptation by unet-gan**
- [[Paper]](https://arxiv.org/pdf/1910.13681.pdf)
- **is the trend of increasing use of patient-reported outcome measures in medical device studies the sign of shift towards value-based purchasing in europe?**
- [[Paper]](https://doi.org/10.1007/s10198-019-01070-1)
- **influence of response shift and disposition on patient-reported outcomes may lead to suboptimal medical decisions: a medical ethics perspective**
- [[Paper]](https://doi.org/10.1186/s12910-019-0397-3)
- **impact of shift duration on alertness among air‐medical emergency care clinician shift workers**
- [[Paper]](https://doi.org/10.1002/ajim.22956)
- **vaccine distribution-equity left behind?**
- [[Paper]](https://www.ncbi.nlm.nih.gov/pubmed/33512381)
- **methods of generating submicrometer phase-shift perfluorocarbon droplets for applications in medical ultrasonography**
- [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7604095)