{"id":57631,"url":"https://github.com/monk1337/Awesome-Robust-Machine-Learning","name":"Awesome-Robust-Machine-Learning","description":"A curated list of Robust Machine Learning papers/articles and recent advancements.","projects_count":906,"last_synced_at":"2026-09-12T15:00:20.179Z","repository":{"id":135213088,"uuid":"548039094","full_name":"monk1337/Awesome-Robust-Machine-Learning","owner":"monk1337","description":"A curated list of Robust Machine Learning papers/articles and recent advancements.","archived":false,"fork":false,"pushed_at":"2022-10-13T04:18:04.000Z","size":467,"stargazers_count":33,"open_issues_count":0,"forks_count":1,"subscribers_count":1,"default_branch":"main","last_synced_at":"2026-08-23T21:45:31.722Z","etag":null,"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"],"latest_commit_sha":null,"homepage":"","language":null,"has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":null,"status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/monk1337.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":null,"code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null}},"created_at":"2022-10-08T20:13:12.000Z","updated_at":"2026-05-20T02:21:01.000Z","dependencies_parsed_at":"2024-04-09T06:15:43.188Z","dependency_job_id":null,"html_url":"https://github.com/monk1337/Awesome-Robust-Machine-Learning","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"purl":"pkg:github/monk1337/Awesome-Robust-Machine-Learning","repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monk1337%2FAwesome-Robust-Machine-Learning","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monk1337%2FAwesome-Robust-Machine-Learning/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monk1337%2FAwesome-Robust-Machine-Learning/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monk1337%2FAwesome-Robust-Machine-Learning/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/monk1337","download_url":"https://codeload.github.com/monk1337/Awesome-Robust-Machine-Learning/tar.gz/refs/heads/main","sbom_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/monk1337%2FAwesome-Robust-Machine-Learning/sbom","scorecard":null,"host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":341189360,"owners_count":37239078,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2026-08-22T15:14:58.755Z","status":"online","status_checked_at":"2026-09-12T02:00:07.213Z","response_time":57,"last_error":null,"robots_txt_status":"success","robots_txt_updated_at":"2025-07-24T06:49:26.215Z","robots_txt_url":"https://github.com/robots.txt","online":true,"can_crawl_api":true,"host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"created_at":"2024-04-08T00:00:22.943Z","updated_at":"2026-09-12T15:00:20.179Z","primary_language":null,"list_of_lists":false,"displayable":true,"categories":["Robust Machine Learning in Medical Domain","Robust machine learning","Code","Tutorials"],"sub_categories":["Distribution shift in question answering","Distribution Shift","Clinical Distribution Shift","Medical Distribution Shift"],"readme":"# Awesome-Robust-Machine-Learning\n\nRobust 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.\n\n\n[![Awesome](https://cdn.rawgit.com/sindresorhus/awesome/d7305f38d29fed78fa85652e3a63e154dd8e8829/media/badge.svg)](https://github.com/sindresorhus/awesome)\n[![PRs Welcome](https://img.shields.io/badge/PRs-welcome-brightgreen.svg?style=flat-square)](http://makeapullrequest.com)\n[![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)\n  \u003cimg width=\"350\" src=\"./images/dishit.png\"\u003e\n\u003c/p\u003e\n\n\n\n##### Table of Contents\n\n1. [Papers](#Robust-Machine-Learning)\n   - [Robust machine learning](#robust-machine-learning)\n   - [Robust Machine Learning in Medical Domain](#robust-machine-learning-in-medical-domain)\n   - [Distribution Shift](#distribution-shift)\n   - [Distribution shift in question answering](#distribution-shift-in-question-answering)\n   - [Clinical Distribution Shift](#clinical-distribution-shift)\n   - [Medical Distribution Shift](#medical-distribution-shift)\n   \n2. [Code](#Code)\n3. [datasets](#Datasets)\n4. [Tutorials](#Tutorials)\n5. [Researchers](#Researchers)\n\n\n## Code\n\n- **Estimating Generalization** \n  - [[Code]](https://github.com/chingyaoc/estimating-generalization)\n  \n- **Adversarial Robustness Toolbox (ART)**\n  - [[Code]](https://github.com/Trusted-AI/adversarial-robustness-toolbox)\n  \n- **Robustness Gym**\n  - [[Code]](https://github.com/robustness-gym/robustness-gym)\n  \n- **WILDS: A benchmark of in-the-wild distribution shifts**\n  - [[Code]](https://github.com/p-lambda/wilds)\n \n  \n## Tutorials\n\n- **CS282R: Robust Machine Learning Workshop**\n  - [[Course]](https://github.com/kojino/Harvard-Robust-Machine-Learning)\n  \n- **Understand Robustness**\n  - [[Course]](https://github.com/Nathanlauga/understand-robustness)\n\n\n## Papers\n\n## Robust machine learning\n\n- **robust machine learning systems: reliability and security for ...**\n  - [[Paper]](https://ieeexplore.ieee.org/document/8474192)\n- **robust machine comprehension models via adversarial ...**\n  - [[Paper]](https://aclanthology.org/N18-2091)\n- **robust distant supervision relation extraction via deep ...**\n  - [[Paper]](https://aclanthology.org/P18-1199)\n- **towards robust neural machine translation - acl anthology**\n  - [[Paper]](https://aclanthology.org/P18-1163)\n- **trainable, scalable summarization using ... - acl anthology**\n  - [[Paper]](https://aclanthology.org/C98-1009.pdf)\n- **learning robust representations of text - acl anthology**\n  - [[Paper]](https://aclanthology.org/D16-1207.pdf)\n- **robust learning, smoothing, and parameter tying on ...**\n  - [[Paper]](https://aclanthology.org/J95-3002.pdf)\n- **robust machine reading comprehension by learning soft ...**\n  - [[Paper]](https://aclanthology.org/2020.coling-main.248)\n- **evaluating neural model robustness for machine ...**\n  - [[Paper]](https://aclanthology.org/2021.eacl-main.210.pdf)\n- **improving robustness of neural machine translation with ...**\n  - [[Paper]](https://aclanthology.org/W19-5368)\n- **learning to reweight examples for robust deep learning**\n  - [[Paper]](https://proceedings.mlr.press/v80/ren18a/ren18a.pdf)\n- **robust learning under uncertain test distributions: relating ...**\n  - [[Paper]](http://proceedings.mlr.press/v32/wen14.html)\n- **robust reinforcement learning: a constrained game ...**\n  - [[Paper]](https://proceedings.mlr.press/v144/yu21a.html)\n- **overfitting in adversarially robust deep learning**\n  - [[Paper]](http://proceedings.mlr.press/v119/rice20a/rice20a.pdf)\n- **robust deep learning as optimal control: insights and ...**\n  - [[Paper]](http://proceedings.mlr.press/v120/seidman20a/seidman20a.pdf)\n- **selfie: refurbishing unclean samples for robust deep ...**\n  - [[Paper]](https://proceedings.mlr.press/v97/song19b.html)\n- **minimax regret optimization for robust machine learning ...**\n  - [[Paper]](https://proceedings.mlr.press/v178/agarwal22b.html)\n- **improving robustness of deep-learning-based image ...**\n  - [[Paper]](https://proceedings.mlr.press/v119/raj20a.html)\n- **efficient and robust automated machine learning**\n- **robustness in machine learning - jerry li**\n  - [[Paper]](https://jerryzli.github.io/robust-ml-fall19.html)\n- **robust ml**\n  - [[Paper]](https://www.robust-ml.org/)\n- **robust machine learning models and their applications**\n  - [[Paper]](https://dspace.mit.edu/handle/1721.1/130760)\n- **robust machine learning - data analytics and ... - tum**\n  - [[Paper]](https://www.cs.cit.tum.de/daml/forschung/robust-machine-learning/)\n- **what is the definition of the robustness of a machine learning ...**\n  - [[Paper]](https://www.researchgate.net/post/What_is_the_definition_of_the_robustness_of_a_machine_learning_algorithm)\n- **robust intelligence**\n  - [[Paper]](https://www.robustintelligence.com/)\n- **robust machine learning | the alan turing institute**\n  - [[Paper]](https://www.turing.ac.uk/research/interest-groups/robust-machine-learning)\n  - [[Paper]](https://papers.nips.cc/paper/5872-efficient-and-robust-automated-machine-learning)\n- **robust reinforcement learning**\n  - [[Paper]](https://papers.nips.cc/paper/1841-robust-reinforcement-learning.pdf)\n- **a closer look at accuracy vs. robustness**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/61d77652c97ef636343742fc3dcf3ba9-Paper.pdf)\n- **provably robust deep learning via adversarially trained ...**\n  - [[Paper]](https://papers.nips.cc/paper/9307-provably-robust-deep-learning-via-adversarially-trained-smoothed-classifiers.pdf)\n- **robust reinforcement learning via adversarial training with ...**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/5cb0e249689cd6d8369c4885435a56c2-Paper.pdf)\n- **robustness of classifiers: from adversarial to random noise**\n  - [[Paper]](http://papers.nips.cc/paper/6331-robustness-of-classifiers-from-adversarial-to-random-noise.pdf)\n- **boundary thickness and robustness in learning models**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/44e76e99b5e194377e955b13fb12f630-Paper.pdf)\n- **a causal view on robustness of neural networks**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/02ed812220b0705fabb868ddbf17ea20-Paper.pdf)\n- **robust reinforcement learning as a stackelberg game via ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0430.pdf)\n- **global robustness evaluation of deep neural networks with ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2019/0824.pdf)\n- **improving the robustness of deep neural networks ... - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2019/403)\n- **robust reinforcement learning as a stackelberg ... - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/430)\n- **on guaranteed optimal robust explanations for nlp models**\n  - [[Paper]](https://www.ijcai.org/proceedings/2021/366)\n- **robust learning from noisy side-information by semidefinite ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2019/349)\n- **towards accurate and robust domain adaptation under ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2020/0314.pdf)\n- **high-robustness, low-transferability fingerprinting of neural ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2021/0080.pdf)\n- **on guaranteed optimal robust explanations for nlp ... - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2021/0366.pdf)\n- **towards robust resnet: a small step but a giant leap - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2019/595)\n- **learning perturbation sets for robust ma**\n  - [[Paper]](https://openreview.net/pdf?id=MIDckA56aD)\n- **analyzing the robustness of open-world machine learning.**\n  - [[Paper]](https://openreview.net/forum?id=laa530q5gUR)\n- **card: certifiably robust machine learning pipeline via ...**\n  - [[Paper]](https://openreview.net/forum?id=roaZrQMGsd6\u0026referrer=%5Bthe%20profile%20of%20Bo%20Li%5D(%2Fprofile%3Fid%3D~Bo_Li19))\n- **sample selection for fair and robust training - openreview**\n  - [[Paper]](https://openreview.net/pdf?id=IZNR0RDtGp3)\n- **robustness between the worst and average case - nips papers**\n  - [[Paper]](https://openreview.net/pdf?id=Y8YqrYeFftd)\n- **machine learning explainability and robustness - openreview**\n  - [[Paper]](https://openreview.net/forum?id=REwtJ_Af00Z)\n- **secure byzantine-robust machine learning | openreview**\n  - [[Paper]](https://openreview.net/forum?id=69EFStdgTD2\u0026referrer=%5Bthe%20profile%20of%20Lie%20He%5D(%2Fprofile%3Fid%3D~Lie_He1))\n- **robust deep reinforcement learning through adversarial loss**\n  - [[Paper]](https://openreview.net/forum?id=eaAM_bdW0Q)\n- **[2101.02559] robust machine learning systems - arxiv**\n  - [[Paper]](https://arxiv.org/abs/2101.02559)\n- **[2112.00639] robustness in deep learning for computer vision**\n  - [[Paper]](https://arxiv.org/abs/2112.00639)\n- **robust machine learning approach for predicting kinase ...**\n  - [[Paper]](https://arxiv.org/abs/2111.08008)\n- **towards efficient data-centric robust machine learning with ...**\n  - [[Paper]](https://arxiv.org/abs/2203.03810)\n- **why robust generalization in deep learning is difficult - arxiv**\n  - [[Paper]](http://arxiv.org/abs/2205.13863)\n- **rethinking machine learning robustness via its link with the ...**\n  - [[Paper]](https://arxiv.org/abs/2202.08944)\n- **sample-efficient training of robust deep learning models**\n  - [[Paper]](https://arxiv.org/abs/2112.02542)\n- **when doubly robust methods meet machine learning ... - arxiv**\n  - [[Paper]](https://arxiv.org/abs/2204.10969)\n- **[2202.05395] robust, deep, and reinforcement learning for ...**\n  - [[Paper]](https://arxiv.org/abs/2202.05395)\n- **robust learning from observation with model misspecification**\n  - [[Paper]](https://arxiv.org/abs/2202.06003)\n- **neural network for determining an asteroid mineral composition from reflectance spectra**\n  - [[Paper]](https://arxiv.org/abs/2210.01006)\n- **understanding adversarial robustness against on-manifold adversarial examples**\n  - [[Paper]](https://arxiv.org/abs/2210.00430)\n- **learning-based design of luenberger observers for autonomous nonlinear systems**\n  - [[Paper]](https://arxiv.org/abs/2210.01476)\n- **interpretable option discovery using deep q-learning and variational autoencoders**\n  - [[Paper]](https://arxiv.org/abs/2210.01231)\n- **secoe: alleviating sensors failure in machine learning-coupled iot systems**\n  - [[Paper]](https://arxiv.org/abs/2210.02144)\n- **robust fair clustering: a novel fairness attack and defense framework**\n  - [[Paper]](https://arxiv.org/abs/2210.01953)\n- **a closer look at robustness to l-infinity and spatial perturbations and their composition**\n  - [[Paper]](https://arxiv.org/abs/2210.02577)\n- **anomaly detection using data depth: multivariate case**\n  - [[Paper]](https://arxiv.org/abs/2210.02851)\n- **flow matching for generative modeling**\n  - [[Paper]](https://arxiv.org/abs/2210.02747)\n- **subspace learning for feature selection via rank revealing qr factorization: unsupervised and hybrid approaches with non-negative matrix factorization and evolutionary algorithm**\n  - [[Paper]](https://arxiv.org/abs/2210.00418)\n- **evaluation of physics constrained data-driven methods for turbulence model uncertainty quantification**\n  - [[Paper]](https://arxiv.org/abs/2210.00002)\n- **latent state marginalization as a low-cost approach for improving exploration**\n  - [[Paper]](https://arxiv.org/abs/2210.00999)\n- **differentiable parsing and visual grounding of verbal instructions for object placement**\n  - [[Paper]](https://arxiv.org/abs/2210.00215)\n- **random data augmentation based enhancement: a generalized enhancement approach for medical datasets**\n  - [[Paper]](https://arxiv.org/abs/2210.00824)\n- **robust self-healing prediction model for high dimensional data**\n  - [[Paper]](https://arxiv.org/abs/2210.01788)\n- **strength-adaptive adversarial training**\n  - [[Paper]](https://arxiv.org/abs/2210.01288)\n- **feddig: robust federated learning using data digest to represent absent clients**\n  - [[Paper]](https://arxiv.org/abs/2210.00737)\n- **distributionally adaptive meta reinforcement learning**\n  - [[Paper]](https://arxiv.org/abs/2210.03104)\n- **blockchain-based monitoring for poison attack detection in decentralized federated learning**\n  - [[Paper]](https://arxiv.org/abs/2210.02873)\n- **ncvx: a general-purpose optimization solver for constrained machine and deep learning**\n  - [[Paper]](https://arxiv.org/abs/2210.00973)\n- **force-aware interface via electromyography for natural vr/ar interaction**\n  - [[Paper]](https://arxiv.org/abs/2210.01225)\n- **tikhonov regularization is optimal transport robust under martingale constraints**\n  - [[Paper]](https://arxiv.org/abs/2210.01413)\n- **multiguard: provably robust multi-label classification against adversarial examples**\n  - [[Paper]](https://arxiv.org/abs/2210.01111)\n- **unsupervised model selection for time-series anomaly detection**\n  - [[Paper]](https://arxiv.org/abs/2210.01078)\n- **perceptual attacks of no-reference image quality models with human-in-the-loop**\n  - [[Paper]](https://arxiv.org/abs/2210.00933)\n- **robust $q$-learning algorithm for markov decision processes under wasserstein uncertainty**\n  - [[Paper]](https://arxiv.org/abs/2210.00898)\n- **stability via adversarial training of neural network stochastic control of mean-field type**\n  - [[Paper]](https://arxiv.org/abs/2210.00874)\n- **comparison of data representations and machine learning architectures for user identification on arbitrary motion sequences**\n  - [[Paper]](https://arxiv.org/abs/2210.00527)\n- **spectral augmentation for self-supervised learning on graphs**\n  - [[Paper]](https://arxiv.org/abs/2210.00643)\n- **causal estimation for text data with (apparent) overlap violations**\n  - [[Paper]](https://arxiv.org/abs/2210.00079)\n- **codedsi: differentiable code search**\n  - [[Paper]](https://arxiv.org/abs/2210.00328)\n- **solving practical multi-body dynamics problems using a single neural operator**\n  - [[Paper]](https://arxiv.org/abs/2210.00222)\n- **on attacking out-domain uncertainty estimation in deep neural networks**\n  - [[Paper]](https://arxiv.org/abs/2210.02191)\n- **stock volatility prediction using time series and deep learning approach**\n  - [[Paper]](https://arxiv.org/abs/2210.02126)\n- **robust estimation of loss-based measures of model performance under covariate shift**\n  - [[Paper]](https://arxiv.org/abs/2210.01980)\n- **meta-ensemble parameter learning**\n  - [[Paper]](https://arxiv.org/abs/2210.01973)\n- **tree mover's distance: bridging graph metrics and stability of graph neural networks**\n  - [[Paper]](https://arxiv.org/abs/2210.01906)\n- **bayesft: bayesian optimization for fault tolerant neural network architecture**\n  - [[Paper]](https://arxiv.org/abs/2210.01795)\n- **paging with succinct predictions**\n  - [[Paper]](https://arxiv.org/abs/2210.02775)\n- **dynamical systems' based neural networks**\n  - [[Paper]](https://arxiv.org/abs/2210.02373)\n- **practical adversarial attacks on spatiotemporal traffic forecasting models**\n  - [[Paper]](https://arxiv.org/abs/2210.02447)\n- **chemalgebra: algebraic reasoning on chemical reactions**\n  - [[Paper]](https://arxiv.org/abs/2210.02095)\n- **null hypothesis test for anomaly detection**\n  - [[Paper]](https://arxiv.org/abs/2210.02226)\n- **rethinking lipschitz neural networks for certified l-infinity robustness**\n  - [[Paper]](https://arxiv.org/abs/2210.01787)\n- **learning robust kernel ensembles with kernel average pooling**\n  - [[Paper]](https://arxiv.org/abs/2210.00062)\n- **rap: risk-aware prediction for robust planning**\n  - [[Paper]](https://arxiv.org/abs/2210.01368)\n- **adaptive weight decay: on the fly weight decay tuning for improving robustness**\n  - [[Paper]](https://arxiv.org/abs/2210.00094)\n- **hip fracture prediction using the first principal component derived from fea-computed fracture loads**\n  - [[Paper]](https://arxiv.org/abs/2210.01032)\n- **uncertainty-aware predictions of molecular x-ray absorption spectra using neural network ensembles**\n  - [[Paper]](https://arxiv.org/abs/2210.00336)\n- **adversarial robustness of representation learning for knowledge graphs**\n  - [[Paper]](https://arxiv.org/abs/2210.00122)\n- **building robust machine learning systems: current progress, research challenges, and opportunities**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3316781.3323472)\n- **building reproducible, reusable, and robust machine learning software**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3401025.3407941)\n- **towards lightweight and robust machine learning for cdn caching**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3286062.3286082)\n- **a robust machine learning technique to predict low-performing students**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3277569)\n- **energy-efficient and adversarially robust machine learning with selective dynamic band filtering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3453688.3461756)\n- **robust machine learning-enabled routing for highly mobile vehicular networks with parrot in ns-3**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3460797.3460810)\n- **the raise of machine learning hyperparameter constraints in python code**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3533767.3534400)\n- **adversarial scrutiny of evidentiary statistical software**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3531146.3533228)\n- **continuous lwe**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3406325.3451000)\n- **rgrecsys: a toolkit for robustness evaluation of recommender systems**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3488560.3502192)\n- **sentiment analysis using xlm-r transformer and zero-shot transfer learning on resource-poor indian language**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3461764)\n- **power-attack: a comprehensive tool-chain for modeling and simulating attacks in power systems**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3470481.3472705)\n- **discovering invariant and changing mechanisms from data**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539479)\n- **the road to explainability is paved with bias: measuring the fairness of explanations**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3531146.3533179)\n- **predicting cognitive load in an emergency simulation based on behavioral and physiological measures**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340555.3353735)\n- **structack: structure-based adversarial attacks on graph neural networks**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3465336.3475110)\n- **learning on the rings: self-supervised 3d finger motion tracking using wearable sensors**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534587)\n- **ensemble learning for effective run-time hardware-based malware detection: a comprehensive analysis and classification**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3195970.3196047)\n- **web-based startup success prediction**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3269206.3272011)\n- **robust federated learning based on metrics learning and unsupervised clustering for malicious data detection**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3476883.3520221)\n- **when video meets inertial sensors: zero-shot domain adaptation for finger motion analytics with inertial sensors**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3450268.3453537)\n- **analyzing hardware based malware detectors**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3061639.3062202)\n- **third workshop on adversarial learning methods for machine learning and data mining (advml 2021)**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3469455)\n- **the fourth workshop on adversarial learning methods for machine learning and data mining (advml 2022)**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3542897)\n- **robust large-scale machine learning in the cloud**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2939672.2939790)\n- **adversarial robustness in deep learning: from practices to theories**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3470812)\n- **machine learning and data cleaning: which serves the other?**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3506712)\n- **a robust mature tomato detection in greenhouse scenes using machine learning and color analysis**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3318299.3318338)\n- **privacy risks of securing machine learning models against adversarial examples**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3319535.3354211)\n- **fair, robust, and data-efficient machine learning in healthcare**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3514094.3539552)\n- **machine learning @ amazon**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2778865.2778867)\n- **debiased-cam to mitigate image perturbations with faithful visual explanations of machine learning**\n  - 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[[Paper]](https://dl.acm.org/doi/pdf/10.1145/3368555.3384458)\n- **analysis of machine learning models predicting quality of life for cancer patients**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3444757.3485103)\n- **diagnosis of methylmalonic acidemia using machine learning methods**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340997.3341000)\n- **assuring the machine learning lifecycle: desiderata, methods, and challenges**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3453444)\n- **bringing machine learning closer to non-experts: proposal of a user-friendly machine learning tool in the healthcare domain**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3486011.3486469)\n- **robust machine learning in critical care — software engineering and medical perspectives**\n  - [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9474414)\n- **open source robust machine learning software for medical patient data analysis and cloud storage**\n  - [[Paper]](https://doi.org/10.1007/978-3-030-64610-3_104)\n- **robust machine learning variable importance analyses of medical conditions for health care spending**\n  - [[Paper]](https://doi.org/10.1111/1475-6773.12848)\n- **robust machine learning against adversarial samples at test time**\n  - [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9149002)\n- **lung cancer prediction using robust machine learning and image enhancement methods on extracted gray-level co-occurrence matrix features**\n  - [[Paper]](https://doi.org/10.3390/app12136517)\n- **a robust machine learning predictive model for maternal health risk**\n  - [[Paper]](http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\u0026arnumber=9885515)\n- **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)**\n  - [[Paper]](https://tel.archives-ouvertes.fr/tel-02954033)\n- **a robust and stable gene selection algorithm based on graph theory and machine learning**\n  - [[Paper]](https://doi.org/10.1186/s40246-021-00366-9)\n\n\n\n### Distribution Shift\n\n\n\n- **difference between distribution shift and data shift, concept ...**\n  - [[Paper]](https://stats.stackexchange.com/questions/548405/difference-between-distribution-shift-and-data-shift-concept-drift-and-model-dr)\n- **understanding dataset shift - towards data science**\n  - [[Paper]](https://towardsdatascience.com/understanding-dataset-shift-f2a5a262a766)\n- **distribution shift framework - deepmind**\n  - [[Paper]](https://www.deepmind.com/open-source/distribution-shift-framework)\n- **4.7. environment and distribution shift**\n  - [[Paper]](https://d2l.ai/chapter_linear-classification/environment-and-distribution-shift.html)\n- **learning to predict and make decisions under distribution shift**\n  - [[Paper]](https://www.ml.cmu.edu/research/phd-dissertation-pdfs/yw4_phd_ml_2021.pdf)\n- **dataset shift in machine learning - mit press**\n  - [[Paper]](https://mitpress.mit.edu/9780262545877/dataset-shift-in-machine-learning/)\n- **mechanical mnist – distribution shift - openbu**\n  - [[Paper]](https://open.bu.edu/handle/2144/44485)\n- **principles of distribution shift (pods) - icml 2022**\n  - [[Paper]](https://icml.cc/Conferences/2022/ScheduleMultitrack?event=13465)\n- **microsoft/distribution-shift-latent-representations - github**\n  - [[Paper]](https://github.com/microsoft/distribution-shift-latent-representations)\n- **neurips distshift workshop 2021 - google sites**\n  - [[Paper]](https://sites.google.com/view/distshift2021)\n- **types of out-of-distribution texts and how to detect them**\n  - [[Paper]](https://aclanthology.org/2021.emnlp-main.835.pdf)\n- **shifted label distribution matters in distantly supervised ...**\n  - [[Paper]](https://aclanthology.org/D19-1397.pdf)\n- **to annotate or not? predicting performance drop under ...**\n  - 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[[Paper]](https://aclanthology.org/2022.acl-long.74)\n- **joint and conditional estimation of tagging and parsing models**\n  - [[Paper]](https://aclanthology.org/P01-1042.pdf)\n- **measure and improve robustness in nlp models: a survey**\n  - [[Paper]](https://aclanthology.org/2022.naacl-main.339.pdf)\n- **unlearn dataset bias in natural language inference by fitting ...**\n  - [[Paper]](https://aclanthology.org/D19-6115)\n- **2022.findings-naacl.13.pdf - acl anthology**\n  - [[Paper]](https://aclanthology.org/2022.findings-naacl.13.pdf)\n- **an investigation of the (in)effectiveness of counterfactually ...**\n  - [[Paper]](https://aclanthology.org/2022.acl-long.256)\n- **methods for estimating and improving robustness of ...**\n  - [[Paper]](https://aclanthology.org/2022.naacl-srw.6.pdf)\n- **evaluating lottery tickets under distributional shifts**\n  - [[Paper]](https://aclanthology.org/D19-6117.pdf)\n- **test-time training can close the natural distribution shift ...**\n  - [[Paper]](https://proceedings.mlr.press/v162/darestani22a.html)\n- **examining and combating spurious features under ...**\n  - [[Paper]](https://proceedings.mlr.press/v139/zhou21g.html)\n- **on distribution shift in learning-based bug detectors**\n  - [[Paper]](https://proceedings.mlr.press/v162/he22a/he22a.pdf)\n- **estimating generalization under distribution shifts via domain ...**\n  - [[Paper]](https://proceedings.mlr.press/v119/chuang20a.html)\n- **a label transformation framework for correcting label shift**\n  - [[Paper]](https://proceedings.mlr.press/v119/guo20d.html)\n- **bayesian adaptation for covariate shift**\n  - [[Paper]](https://papers.nips.cc/paper/2021/hash/07ac7cd13fd0eb1654ccdbd222b81437-Abstract.html)\n- **rethinking importance weighting for deep learning under ...**\n  - [[Paper]](https://papers.nips.cc/paper/2020/hash/8b9e7ab295e87570551db122a04c6f7c-Abstract.html)\n- **characterizing generalization under out-of-distribution shifts ...**\n  - [[Paper]](https://papers.nips.cc/paper/2021/file/d1f255a373a3cef72e03aa9d980c7eca-Paper.pdf)\n- **a unified view of label shift estimation - nips papers**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/219e052492f4008818b8adb6366c7ed6-Paper.pdf)\n- **provably efficient q-learning with function approximation via ...**\n  - [[Paper]](http://papers.nips.cc/paper/9018-provably-efficient-q-learning-with-function-approximation-via-distribution-shift-error-checking-oracle.pdf)\n- **robust federated learning: the case of affine distribution ...**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/f5e536083a438cec5b64a4954abc17f1-Paper.pdf)\n- **domain adaptation by using causal inference to predict ...**\n  - [[Paper]](https://papers.nips.cc/paper/8282-domain-adaptation-by-using-causal-inference-to-predict-invariant-conditional-distributions.pdf)\n- **domain adaptation with conditional distribution matching and ...**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/dfbfa7ddcfffeb581f50edcf9a0204bb-Paper.pdf)\n- **correcting covariate shift with the frank-wolfe algorithm**\n  - [[Paper]](https://www.ijcai.org/Proceedings/15/Papers/147.pdf)\n- **few-shot adaptation of pre-trained networks for domain shift**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0232.pdf)\n- **distance metric learning under covariate shift - ijcai**\n  - [[Paper]](https://www.ijcai.org/Proceedings/11/Papers/205.pdf)\n- **domain generalization through the lens of angular invariance**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0139.pdf)\n- **approximate exploitability: learning a best response - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0484.pdf)\n- **searching for optimal subword tokenization in cross ... - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/595)\n- **robust domain adaptation: representations, weights and ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2021/0644.pdf)\n- **domain generalization under conditional and label shifts via ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2021/122)\n- **identifying instance features for capability-oriented evaluation**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0392.pdf)\n- **adversarial bi-regressor network for domain adaptive ... - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0501.pdf)\n- **task-aware lipschitz data augmentation for visual ... - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0514.pdf)\n- **topological uncertainty: monitoring trained neural networks ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2021/0367.pdf)\n- **test-time fourier style calibration for domain generalization**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0240.pdf)\n- **self-managing associative memory for dynamic acquisition of ...**\n  - [[Paper]](https://www.ijcai.org/Proceedings/09/Papers/169.pdf)\n- **learning personalization for cross-silo federated learning**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/301)\n- **a fine-grained analysis on distribution shift - openreview**\n  - [[Paper]](https://openreview.net/forum?id=Dl4LetuLdyK)\n- **a benchmark of in-the-wild distribution shift over time**\n  - [[Paper]](https://openreview.net/forum?id=BUQD1tJ2UwK)\n- **feature shift detection: localizing which ... - nips papers**\n  - [[Paper]](https://openreview.net/pdf?id=k1YfNKf0FZn)\n- **online adaptation to label distribution shift - openreview**\n  - [[Paper]](https://openreview.net/forum?id=6h14cMLgb5q)\n- **if your data distribution shifts, use self-learning - openreview**\n  - [[Paper]](https://openreview.net/forum?id=1oEvY1a67c1)\n- **how robust are pre-trained models to distribution shift?**\n  - [[Paper]](https://openreview.net/forum?id=zKDcZBVVEWm)\n- **fair predictors under distribution shift - openreview**\n  - [[Paper]](https://openreview.net/forum?id=zgl6DdYHggo)\n- **an empirical study of methods for detecting dataset shift**\n  - [[Paper]](https://openreview.net/pdf/f9a2ae9ee8021aeb70a8f2deeab247a324b8200e.pdf)\n- **robust generalization despite distribution shift via minimum ...**\n  - [[Paper]](https://openreview.net/attachment?id=kiWRlrbVzSM\u0026name=supplementary_material)\n- **tracking the risk of a deployed model - openreview**\n  - [[Paper]](https://openreview.net/pdf?id=Ro_zAjZppv)\n- **towards explaining image-based distribution shifts**\n  - 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arxiv**\n  - [[Paper]](https://arxiv.org/abs/2206.08871)\n- **calibrated ensembles can mitigate accuracy tradeoffs under ...**\n  - [[Paper]](http://arxiv.org/abs/2207.08977)\n- **discovering distribution shifts using latent space ... - arxiv**\n  - [[Paper]](https://arxiv.org/abs/2202.02339)\n- **models out of line: a fourier lens on distribution shift ... - arxiv**\n  - [[Paper]](https://arxiv.org/abs/2207.04075)\n- **combating distribution shift for accurate time series ... - arxiv**\n  - [[Paper]](https://arxiv.org/abs/2202.10808)\n- **time series prediction under distribution shift using ... - arxiv**\n  - [[Paper]](http://arxiv.org/abs/2207.11486)\n- **on distribution shift in learning-based bug detectors - arxiv**\n  - [[Paper]](https://arxiv.org/abs/2204.10049)\n- **fairness transferability subject to bounded distribution shift**\n  - [[Paper]](https://arxiv.org/abs/2206.00129)\n- **combating label distribution shift for active domain adaptation**\n  - [[Paper]](https://arxiv.org/abs/2208.06604)\n- **test: test-time self-training under distribution shift - arxiv**\n  - [[Paper]](https://arxiv.org/abs/2209.11459)\n- **[2202.06523] metashift: a dataset of datasets for evaluating ...**\n  - [[Paper]](https://arxiv.org/abs/2202.06523)\n- **estimating test performance for ai medical devices under ...**\n  - [[Paper]](https://arxiv.org/abs/2207.05796)\n- **agreement-on-the-line: predicting the performance of neural ...**\n  - [[Paper]](https://arxiv.org/abs/2206.13089)\n- **contrastive graph few-shot learning**\n  - [[Paper]](https://arxiv.org/abs/2210.00084)\n- **learning an invertible output mapping can mitigate simplicity bias in neural networks**\n  - [[Paper]](https://arxiv.org/abs/2210.01360)\n- **a fixed-point algorithm for the ac power flow problem**\n  - [[Paper]](https://arxiv.org/abs/2210.01310)\n- **percolation properties of the neutron population in nuclear reactors**\n  - [[Paper]](https://arxiv.org/abs/2210.02413)\n- **a review of uncertainty calibration in pretrained object detectors**\n  - [[Paper]](https://arxiv.org/abs/2210.02935)\n- **analysis of irs-assisted downlink wireless networks over generalized fading**\n  - [[Paper]](https://arxiv.org/abs/2210.02717)\n- **exploring effective knowledge transfer for few-shot object detection**\n  - [[Paper]](https://arxiv.org/abs/2210.02021)\n- **gapx: generalized autoregressive paraphrase-identification x**\n  - [[Paper]](https://arxiv.org/abs/2210.01979)\n- **gravitational microlensing by dressed primordial black holes**\n  - [[Paper]](https://arxiv.org/abs/2210.02078)\n- **data drift correction via time-varying importance weight estimator**\n  - [[Paper]](https://arxiv.org/abs/2210.01422)\n- **adawac: adaptively weighted augmentation consistency regularization for volumetric medical image segmentation**\n  - [[Paper]](https://arxiv.org/abs/2210.01891)\n- **on the (un)importance of the transition-dipole phase in the high-harmonic generation from solid state media**\n  - [[Paper]](https://arxiv.org/abs/2210.02500)\n- **satellite-based continuous-variable quantum key distribution under the earth's gravitational field**\n  - [[Paper]](https://arxiv.org/abs/2210.02776)\n- **env-aware anomaly detection: ignore style changes, stay true to content!**\n  - [[Paper]](https://arxiv.org/abs/2210.03103)\n- **spectroscopic localization of atomic sample plane for precise digital holography**\n  - [[Paper]](https://arxiv.org/abs/2210.02721)\n- **benchmarking learnt radio localisation under distribution shift**\n  - [[Paper]](https://arxiv.org/abs/2210.01930)\n- **generalized solution of the paraxial equation**\n  - [[Paper]](https://arxiv.org/abs/2210.01088)\n- **on the effects of data normalisation for domain adaptation on eeg data**\n  - [[Paper]](https://arxiv.org/abs/2210.01081)\n- **spectral-domain method of moments analysis of spatially dispersive graphene patch embedded in planarly layered media**\n  - [[Paper]](https://arxiv.org/abs/2210.00877)\n- **towards performance portable programming for distributed heterogeneous systems**\n  - [[Paper]](https://arxiv.org/abs/2210.01238)\n- **simper: simple self-supervised learning of periodic targets**\n  - [[Paper]](https://arxiv.org/abs/2210.03115)\n- **class-specific channel attention for few-shot learning**\n  - [[Paper]](https://arxiv.org/abs/2209.01332)\n- **deep stable representation learning on electronic health records**\n  - [[Paper]](https://arxiv.org/abs/2209.01321)\n- **a demonstration of over-the-air computation for federated edge learning**\n  - [[Paper]](https://arxiv.org/abs/2209.09954)\n- **s2p: state-conditioned image synthesis for data augmentation in offline reinforcement learning**\n  - [[Paper]](https://arxiv.org/abs/2209.15256)\n- **domain generalization -- a causal perspective**\n  - [[Paper]](https://arxiv.org/abs/2209.15177)\n- **a domain adaptive deep learning solution for scanpath prediction of paintings**\n  - [[Paper]](https://arxiv.org/abs/2209.11338)\n- **robust bayesian non-segmental detection of multiple change-points**\n  - [[Paper]](https://arxiv.org/abs/2209.14995)\n- **modelling the energy distribution in chime/frb catalog-1**\n  - [[Paper]](https://arxiv.org/abs/2209.12961)\n- **exact and efficient multivariate two-sample tests through generalized linear rank statistics**\n  - [[Paper]](https://arxiv.org/abs/2209.14235)\n- **black-box audits for group distribution shifts**\n  - [[Paper]](https://arxiv.org/abs/2209.03620)\n- **semi-supervised triply robust inductive transfer learning**\n  - [[Paper]](https://arxiv.org/abs/2209.04977)\n- **polito-iit-cini submission to the epic-kitchens-100 unsupervised domain adaptation challenge for action recognition**\n  - [[Paper]](https://arxiv.org/abs/2209.04525)\n- **non-parametric temporal adaptation for social media topic classification**\n  - [[Paper]](https://arxiv.org/abs/2209.05706)\n- **test-time adaptation with principal component analysis**\n  - [[Paper]](https://arxiv.org/abs/2209.05779)\n- **giant overreflection of magnetohydrodynamic waves from inhomogeneous plasmas with nonuniform shear flows**\n  - [[Paper]](https://arxiv.org/abs/2209.08061)\n- **mitigating both covariate and conditional shift for domain generalization**\n  - [[Paper]](https://arxiv.org/abs/2209.08253)\n- **visible-infrared person re-identification using privileged intermediate information**\n  - [[Paper]](https://arxiv.org/abs/2209.09348)\n- **improving replay-based continual semantic segmentation with smart data selection**\n  - [[Paper]](https://arxiv.org/abs/2209.09839)\n- **towards optimization and model selection for domain generalization: a mixup-guided solution**\n  - [[Paper]](https://arxiv.org/abs/2209.00652)\n- **future gradient descent for adapting the temporal shifting data distribution in online recommendation systems**\n  - [[Paper]](https://arxiv.org/abs/2209.01143)\n- **meta-learning with less forgetting on large-scale non-stationary task distributions**\n  - [[Paper]](https://arxiv.org/abs/2209.01501)\n- **robustness and invariance properties of image classifiers**\n  - [[Paper]](https://arxiv.org/abs/2209.02408)\n- **importance tempering: group robustness for overparameterized models**\n  - [[Paper]](https://arxiv.org/abs/2209.08745)\n- **exploiting instance-based mixed sampling via auxiliary source domain supervision for domain-adaptive action detection**\n  - [[Paper]](https://arxiv.org/abs/2209.15439)\n- **too fine or too coarse? the goldilocks composition of data complexity for robust left-right eye-tracking classifiers**\n  - [[Paper]](https://arxiv.org/abs/2209.03761)\n- **record: resource constrained semi-supervised learning under distribution shift**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3394486.3403214)\n- **towards reliable multimodal stress detection under distribution shift**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3461615.3486570)\n- **confidence may cheat: self-training on graph neural networks under distribution shift**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3485447.3512172)\n- **active model adaptation under unknown shift**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539262)\n- **balance-subsampled stable prediction across unknown test data**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477052)\n- **focused context balancing for robust offline policy evaluation**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330852)\n- **an empirical study on data distribution-aware test selection for deep learning enhancement**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3511598)\n- **a critical reassessment of the saerens-latinne-decaestecker algorithm for posterior probability adjustment**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3433164)\n- **understanding the effect of out-of-distribution examples and interactive explanations on human-ai decision making**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3479552)\n- **co-training disentangled domain adaptation network for leveraging popularity bias in recommenders**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531952)\n- **causpref: causal preference learning for out-of-distribution recommendation**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3485447.3511969)\n- **off-policy actor-critic for recommender systems**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3523227.3546758)\n- **hybridrepair: towards annotation-efficient repair for deep learning models**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3533767.3534408)\n- **decoupled reinforcement learning to stabilise intrinsically-motivated exploration**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3535850.3535978)\n- **neural statistics for click-through rate prediction**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531762)\n- **influence function for unbiased recommendation**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401321)\n- **dcaf-bert: a distilled cachable adaptable factorized model for improved ads ctr prediction**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3524206)\n- **making adversarially-trained language models forget with model retraining: a case study on hate speech detection**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3524667)\n- **causal discovery from heterogeneous/nonstationary data**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3455716.3455805)\n- **svem: a signal variation elimination model for eeg emotion recognition**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3543081.3543085)\n- **adarnn: adaptive learning and forecasting of time series**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3459637.3482315)\n- **transfer string kernel for cross-context dna-protein binding prediction**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1109/TCBB.2016.2609918)\n- **trustworthy graph learning: reliability, explainability, and privacy protection**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3542597)\n- **real negatives matter: continuous training with real negatives for delayed feedback modeling**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3467086)\n- **a new generation of perspective api: efficient multilingual character-level transformers**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539147)\n- **robust self-supervised structural graph neural network for social network prediction**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3485447.3512182)\n- **stable prediction across unknown environments**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3219819.3220082)\n- **learning security classifiers with verified global robustness properties**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3460120.3484776)\n- **fairness violations and mitigation under covariate shift**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3442188.3445865)\n- **quantifying the performance of adversarial training on language models with distribution shifts**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3494108.3522764)\n- **fedrs: federated learning with restricted softmax for label distribution non-iid data**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3447548.3467254)\n- **case study on distribution strategy through biclustering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3387263.3387271)\n- **semi-supervised flexible joint distribution adaptation**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3375998.3376022)\n- **analysis of the modified weibull distribution for estimation of wind speed distribution**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2832987.2833059)\n- **l-shift: encoding and shifting material properties and functionalities with phase-shifting liquid**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2677199.2688807)\n- **embedded out-of-distribution detection on an autonomous robot platform**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3445034.3460509)\n- **distribution-matching embedding for visual domain adaptation**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/2946645.3007061)\n- **transfer learning with dynamic distribution adaptation**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3360309)\n- **probabilistic modeling for frequency vectors using a flexible shifted-scaled dirichlet distribution prior**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3406242)\n- **monitoring perception reliability in autonomous driving: distributional shift detection for estimating the impact of input data on prediction accuracy**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3488904.3493382)\n- **dynamical origins of distribution functions**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330842)\n- **ignis: scaling distribution-oblivious systems with light-touch distribution**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3314221.3314586)\n- **control of networked traffic flow distribution: a stochastic distribution system perspective**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3109761.3158411)\n- **modeling neurocognitive reaction time with gamma distribution**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3167918.3167941)\n- **a feedback shift correction in predicting conversion rates under delayed feedback**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3366423.3380032)\n- **a crowdsourcing approach for the inference of distribution grids**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3208903.3208927)\n- **adapting covariate shift for legal ai**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3322640.3326720)\n- **semi-supervised drifted stream learning with short lookback**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3534678.3539297)\n- **on the theory of policy gradient methods: optimality, approximation, and distribution shift**\n  - [[Paper]](http://jmlr.org/papers/v22/19-736.html)\n- **test-time adaptation to distribution shift by confidence maximization and input transformation**\n  - [[Paper]](https://arxiv.org/pdf/2106.14999.pdf)\n- **mandoline: model evaluation under distribution shift**\n  - [[Paper]](https://arxiv.org/pdf/2107.00643.pdf)\n- **examining and combating spurious features under distribution shift**\n  - [[Paper]](https://arxiv.org/pdf/2106.07171.pdf)\n- **stable prediction with model misspecification and agnostic distribution shift**\n  - [[Paper]](https://arxiv.org/pdf/2001.11713.pdf)\n- **rethinking importance weighting for deep learning under distribution shift**\n  - [[Paper]](https://arxiv.org/pdf/2006.04662.pdf)\n- **online adaptation to label distribution shift**\n  - [[Paper]](https://arxiv.org/pdf/2107.04520.pdf)\n- **near-optimal linear regression under distribution shift**\n  - [[Paper]](https://arxiv.org/pdf/2106.12108.pdf)\n- **closing the closed-loop distribution shift in safe imitation learning**\n  - [[Paper]](no_link_found)\n\n\n\n### Distribution shift in question answering\n\n\n- **the effect of natural distribution shift on question answering models ...**\n  - [[Paper]](https://dl.acm.org/doi/abs/10.5555/3524938.3525579)\n- **the effect of natural distribution shift on ... - researchgate**\n  - [[Paper]](https://www.researchgate.net/publication/341069459_The_Effect_of_Natural_Distribution_Shift_on_Question_Answering_Models)\n- **the effect of natural distribution shift on ... - karl krauth**\n  - [[Paper]](https://karlk.net/publication/dist_shift/)\n- **out-of-domain question answering - stanford university**\n  - [[Paper]](https://web.stanford.edu/class/archive/cs/cs224n/cs224n.1214/reports/final_reports/report163.pdf)\n- **selective question answering under domain shift**\n  - [[Paper]](https://aclanthology.org/2020.acl-main.503.pdf)\n- **topic transferable table question answering - acl anthology**\n  - [[Paper]](https://aclanthology.org/2021.emnlp-main.342.pdf)\n- **re-evaluating conversational question answering**\n  - [[Paper]](https://aclanthology.org/2022.acl-long.555.pdf)\n- **improving calibration in question answering - acl anthology**\n  - [[Paper]](https://aclanthology.org/2021.findings-acl.172.pdf)\n- **improving question answering model robustness with ...**\n  - [[Paper]](https://aclanthology.org/2021.emnlp-main.696.pdf)\n- **contrastive domain adaptation for question answering using ...**\n  - [[Paper]](https://aclanthology.org/2021.emnlp-main.754.pdf)\n- **on the efficacy of adversarial data collection for question ...**\n  - [[Paper]](https://aclanthology.org/2021.acl-long.517.pdf)\n- **entity-based knowledge conflicts in question answering**\n  - [[Paper]](https://aclanthology.org/2021.emnlp-main.565.pdf)\n- **improving unsupervised question answering via ...**\n  - [[Paper]](https://aclanthology.org/2021.emnlp-main.340.pdf)\n- **challenges in generalization in open domain question ...**\n  - [[Paper]](https://aclanthology.org/2022.findings-naacl.155.pdf)\n- **accuracy on the line: on the strong correlation between out ...**\n  - [[Paper]](https://proceedings.mlr.press/v139/miller21b/miller21b.pdf)\n- **wilds: a benchmark of in-the-wild distribution shifts**\n  - [[Paper]](http://proceedings.mlr.press/v139/koh21a/koh21a.pdf)\n- **instabilities of offline rl with pre-trained neural ...**\n  - [[Paper]](http://proceedings.mlr.press/v139/wang21z/wang21z.pdf)\n- **data determines distributional robustness in contrastive ...**\n  - [[Paper]](https://proceedings.mlr.press/v162/fang22a/fang22a.pdf)\n- **lyapunov density models: constraining distribution shift in ...**\n  - [[Paper]](https://proceedings.mlr.press/v162/kang22a/kang22a.pdf)\n- **active learning under label shift**\n  - [[Paper]](http://proceedings.mlr.press/v130/zhao21b/zhao21b.pdf)\n- **predicting out-of-distribution error with the projection norm**\n  - [[Paper]](https://proceedings.mlr.press/v162/yu22i/yu22i.pdf)\n- **can autonomous vehicles identify, recover from,and adapt ...**\n  - [[Paper]](http://proceedings.mlr.press/v119/filos20a/filos20a.pdf)\n- **measuring robustness to natural distribution shifts in image ...**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/d8330f857a17c53d217014ee776bfd50-Paper.pdf)\n- **adaptive conformal inference under distribution shift**\n  - [[Paper]](https://papers.nips.cc/paper/2021/file/0d441de75945e5acbc865406fc9a2559-Paper.pdf)\n- **overparameterization improves robustness to covariate shift ...**\n  - [[Paper]](https://papers.nips.cc/paper/2021/file/73fed7fd472e502d8908794430511f4d-Paper.pdf)\n- **revisiting the calibration of modern neural networks**\n  - [[Paper]](https://papers.nips.cc/paper/2021/file/8420d359404024567b5aefda1231af24-Paper.pdf)\n- **reducing unimodal biases for visual question answering**\n  - [[Paper]](https://papers.nips.cc/paper/8371-rubi-reducing-unimodal-biases-for-visual-question-answering.pdf)\n- **debiased visual question answering from feature and ...**\n  - [[Paper]](https://papers.nips.cc/paper/2021/file/1f4477bad7af3616c1f933a02bfabe4e-Paper.pdf)\n- **on the value of out-of-distribution testing: an example of ...**\n  - [[Paper]](https://papers.nips.cc/paper/2020/file/045117b0e0a11a242b9765e79cbf113f-Paper.pdf)\n- **mitigating covariate shift in imitation learning via offline data ...**\n  - [[Paper]](https://papers.nips.cc/paper/2021/file/07d5938693cc3903b261e1a3844590ed-Paper.pdf)\n- **react: out-of-distribution detection with rectified activations**\n  - [[Paper]](https://papers.nips.cc/paper/2021/file/01894d6f048493d2cacde3c579c315a3-Paper.pdf)\n- **learning imbalanced datasets with label-distribution-aware ...**\n  - [[Paper]](https://papers.nips.cc/paper/8435-learning-imbalanced-datasets-with-label-distribution-aware-margin-loss.pdf)\n- **ai foundations for human visual perception driven cognitive ...**\n  - [[Paper]](http://www.ijcai.org/Proceedings/16/Papers/374.pdf)\n- **k-best: a new method for real-time decision making - ijcai**\n  - [[Paper]](https://www.ijcai.org/Proceedings/95-1/Papers/030.pdf)\n- **provable guarantees on the robustness of decision rules to ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2021/0585.pdf)\n- **robustifying vision transformer without retraining from ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0141.pdf)\n- **exploiting the sign of the advantage function to learn ... - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2019/0625.pdf)\n- **bridging causality and learning: how do they benefit from ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2020/0725.pdf)\n- **towards robust dense retrieval via local ranking alignment**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0275.pdf)\n- **robustness guarantees for credal bayesian networks ... - ijcai**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0677.pdf)\n- **multi-agent concentrative coordination with decentralized ...**\n  - [[Paper]](https://www.ijcai.org/proceedings/2022/0085.pdf)\n- **session no. 13 computer understanding ii (representation)**\n  - [[Paper]](http://www.ijcai.org/Proceedings/71/Papers/048.pdf)\n- **an information-theoretic approach to distribution shifts**\n  - [[Paper]](https://openreview.net/forum?id=GrZmKDYCp6H)\n- **a closer look at distribution shifts and out-of ... - openreview**\n  - [[Paper]](https://openreview.net/forum?id=2JFVnWuvrvV)\n- **a fine-grained analysis on distribution shift**\n  - 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arxiv**\n  - [[Paper]](https://arxiv.org/pdf/2201.00299)\n- **toward a fine-grained analysis of distribution shifts in ... - arxiv**\n  - [[Paper]](https://arxiv.org/pdf/2205.02870)\n- **arxiv:2006.05121v3 [cs.cv] 7 apr 2021**\n  - [[Paper]](https://arxiv.org/pdf/2006.05121)\n- **afine-grained analysis on distribution shift - arxiv**\n  - [[Paper]](https://arxiv.org/pdf/2110.11328)\n- **arxiv:2207.08739v1 [cs.cv] 18 jul 2022**\n  - [[Paper]](https://arxiv.org/pdf/2207.08739)\n- **x-ggm: graph generative modeling for out-of-distribution ...**\n  - [[Paper]](https://arxiv.org/pdf/2107.11576)\n- **arxiv:2207.01168v1 [cs.lg] 4 jul 2022**\n  - [[Paper]](https://arxiv.org/pdf/2207.01168)\n- **how good are deep models in understanding\\\\ the generated ...**\n  - [[Paper]](https://arxiv.org/abs/2208.10760)\n- **task formulation matters when learning continually: a case study in visual question answering**\n  - [[Paper]](https://arxiv.org/abs/2210.00044)\n- **complexity-based prompting for multi-step reasoning**\n  - [[Paper]](https://arxiv.org/abs/2210.00720)\n- **test-time adaptation for visual document understanding**\n  - [[Paper]](https://arxiv.org/abs/2206.07240)\n- **data determines distributional robustness in contrastive language image pre-training (clip)**\n  - [[Paper]](https://arxiv.org/abs/2205.01397)\n- **rethinking evaluation practices in visual question answering: a case study on out-of-distribution generalization**\n  - [[Paper]](https://arxiv.org/abs/2205.12191)\n- **teaching models to express their uncertainty in words**\n  - [[Paper]](https://arxiv.org/abs/2205.14334)\n- **improving passage retrieval with zero-shot question generation**\n  - [[Paper]](https://arxiv.org/abs/2204.07496)\n- **data-suite: data-centric identification of in-distribution incongruous examples**\n  - [[Paper]](https://arxiv.org/abs/2202.08836)\n- **on a class of lacunary almost newman polynomials modulo p and density theorems**\n  - [[Paper]](https://arxiv.org/abs/2201.03233)\n- **question generation for evaluating cross-dataset shifts in multi-modal grounding**\n  - [[Paper]](https://arxiv.org/abs/2201.09639)\n- **local distributional chaos**\n  - [[Paper]](https://arxiv.org/abs/2112.01457)\n- **causal forecasting:generalization bounds for autoregressive models**\n  - [[Paper]](https://arxiv.org/abs/2111.09831)\n- **dair: data augmented invariant regularization**\n  - [[Paper]](https://arxiv.org/abs/2110.11205)\n- **topic transferable table question answering**\n  - [[Paper]](https://arxiv.org/abs/2109.07377)\n- **aggregate or not? exploring where to privatize in dnn based federated learning under different non-iid scenes**\n  - [[Paper]](https://arxiv.org/abs/2107.11954)\n- **pointer value retrieval: a new benchmark for understanding the limits of neural network generalization**\n  - [[Paper]](https://arxiv.org/abs/2107.12580)\n- **invariance principle meets information bottleneck for out-of-distribution generalization**\n  - [[Paper]](https://arxiv.org/abs/2106.06607)\n- **a winning hand: compressing deep networks can improve out-of-distribution robustness**\n  - [[Paper]](https://arxiv.org/abs/2106.09129)\n- **approximate bayesian computation for an explicit-duration hidden markov model of covid-19 hospital trajectories**\n  - [[Paper]](https://arxiv.org/abs/2105.00773)\n- **crossnorm and selfnorm for generalization under distribution shifts**\n  - [[Paper]](https://arxiv.org/abs/2102.02811)\n- **a new tool for precise mapping of local temperature fields in submicrometer aqueous volumes**\n  - [[Paper]](https://arxiv.org/abs/2102.07461)\n- **leveraging uncertainty from deep learning for trustworthy materials discovery workflows**\n  - [[Paper]](https://arxiv.org/abs/2012.01478)\n- **coping with label shift via distributionally robust optimisation**\n  - [[Paper]](https://arxiv.org/abs/2010.12230)\n- **some factors of nonsingular bernoulli shifts**\n  - [[Paper]](https://arxiv.org/abs/2010.04636)\n- **a tale of two cities: software developers working from home during the covid-19 pandemic**\n  - [[Paper]](https://arxiv.org/abs/2008.11147)\n- **roses are red, violets are blue... but should vqa expect them to?**\n  - [[Paper]](https://arxiv.org/abs/2006.05121)\n- **generalized mean shift with triangular kernel profile**\n  - [[Paper]](https://arxiv.org/abs/2001.02165)\n- **metaci: meta-learning for causal inference in a heterogeneous population**\n  - [[Paper]](https://arxiv.org/abs/1912.03960)\n- **stationary distributions for the voter model in $d\\geq 3$ are factors of iid**\n  - [[Paper]](https://arxiv.org/abs/1908.09450)\n- **dark matter haloes and subhaloes**\n  - [[Paper]](https://arxiv.org/abs/1907.11775)\n- **information-theoretic considerations in batch reinforcement learning**\n  - [[Paper]](https://arxiv.org/abs/1905.00360)\n- **biobert: a pre-trained biomedical language representation model for biomedical text mining**\n  - [[Paper]](https://arxiv.org/abs/1901.08746)\n- **maximum of branching brownian motion in a periodic environment**\n  - [[Paper]](https://arxiv.org/abs/1812.04189)\n- **density estimation for shift-invariant multidimensional distributions**\n  - [[Paper]](https://arxiv.org/abs/1811.03744)\n- **thick points of random walk and the gaussian free field**\n  - [[Paper]](https://arxiv.org/abs/1809.04369)\n- **koala: a new paradigm for election coverage**\n  - [[Paper]](https://arxiv.org/abs/1807.09665)\n- **accounting for phenology in the analysis of animal movement**\n  - [[Paper]](https://arxiv.org/abs/1806.09473)\n- **when can multi-site datasets be pooled for regression? hypothesis tests, $\\ell_2$-consistency and neuroscience applications**\n  - [[Paper]](https://arxiv.org/abs/1709.00640)\n- **distributed strong diameter network decomposition**\n  - [[Paper]](https://arxiv.org/abs/1602.05437)\n- **achieving delay rate-function optimality in ofdm downlink with time-correlated channels**\n  - [[Paper]](https://arxiv.org/abs/1601.06241)\n- **lyman alpha emitting galaxies in the nearby universe**\n  - [[Paper]](https://arxiv.org/abs/1505.07483)\n- **experimental round-robin differential phase-shift quantum key distribution**\n  - [[Paper]](https://arxiv.org/abs/1505.08142)\n- **constraints on planet occurrence around nearby mid-to-late m dwarfs from the mearth project**\n  - [[Paper]](https://arxiv.org/abs/1307.3178)\n- **x-ggm: graph generative modeling for out-of-distribution generalization in visual question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3474085.3475350)\n- **question rewriting for conversational question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3437963.3441748)\n- **passage similarity and diversification in non-factoid question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3471158.3472249)\n- **conversational question answering over passages by leveraging word proximity networks**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401399)\n- **adapting visual question answering models for enhancing multimodal community q\u0026a platforms**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3357384.3358000)\n- **select, substitute, search: a new benchmark for knowledge-augmented visual question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3404835.3463259)\n- **sanitizing synthetic training data generation for question answering over knowledge graphs**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3409256.3409836)\n- **naranjo question answering using end-to-end multi-task learning model**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3292500.3330770)\n- **meaningful answer generation of e-commerce question-answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3432689)\n- **attentive history selection for conversational question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3357384.3357905)\n- **answer interaction in non-factoid question answering systems**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3295750.3298946)\n- **product-aware answer generation in e-commerce question-answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3289600.3290992)\n- **a discourse-based approach for arabic question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2988238)\n- **verification of the expected answer type for biomedical question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3184558.3191542)\n- **spatiotemporal-textual co-attention network for video question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3320061)\n- **quantifying human-perceived answer utility in non-factoid question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3406522.3446028)\n- **answer ranking based on named entity types for question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3022227.3022297)\n- **adversarial learning of answer-related representation for visual question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3269206.3271765)\n- **video question answering via knowledge-based progressive spatial-temporal attention network**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3321505)\n- **opinion-aware answer generation for review-driven question answering in e-commerce**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3340531.3411904)\n- **xalgo: a design probe of explaining algorithms’ internal states via question-answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397481.3450676)\n- **scaling up online question answering via similar question retrieval**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2876034.2893428)\n- **cross-domain knowledge distillation for retrieval-based question answering systems**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3442381.3449814)\n- **distributed deep learning for question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2983323.2983377)\n- **a non-factoid question-answering taxonomy**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531926)\n- **social question answering: textual, user, and network features for best answer prediction**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2948063)\n- **automatically extracting high-quality negative examples for answer selection in question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3077136.3080645)\n- **dynamic graph reasoning for conversational open-domain question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3498557)\n- **conversational question answering on heterogeneous sources**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531815)\n- **open-retrieval conversational question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3397271.3401110)\n- **fast parameter adaptation for few-shot image captioning and visual question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3240508.3240527)\n- **a corpus for hybrid question answering systems**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3184558.3191540)\n- **explainable conversational question answering over heterogeneous sources**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3477495.3531688)\n- **temporal question answering in news article collections**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3487553.3526023)\n- **asking for help in community question-answering: the goal-framing effect of question expression on response networks**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3529372.3533287)\n- **complex temporal question answering on knowledge graphs**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3459637.3482416)\n- **qanswer: towards question answering search over websites**\n  - 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[[Paper]](https://dl.acm.org/doi/pdf/10.1145/3011549.3011554)\n- **performance prediction for non-factoid question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3341981.3344249)\n- **non-factoid question answering in the legal domain**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/3331184.3331431)\n- **natural language question answering in the financial domain**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.5555/3291291.3291311)\n- **more accurate question answering on freebase**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2806416.2806472)\n- **table cell search for question answering**\n  - [[Paper]](https://dl.acm.org/doi/pdf/10.1145/2872427.2883080)\n- **the effect of natural distribution shift on question answering models**\n  - [[Paper]](https://arxiv.org/pdf/2004.14444.pdf)\n- **a survey of causality in visual question answering**\n  - [[Paper]](https://gaoxiangluo.github.io/files/A_Survey_of_Causality_in_Visual_Question_Answering.pdf)\n- **learning neural models for natural language processing in the face of distributional shift**\n  - 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chip huyen**\n  - [[Paper]](https://huyenchip.com/2022/02/07/data-distribution-shifts-and-monitoring.html)\n- **shifting the distribution | evidence for population health**\n  - [[Paper]](https://academic.oup.com/book/9251/chapter/155945428)\n- **principles for tackling distribution shift - youtube**\n  - [[Paper]](https://www.youtube.com/watch?v=QKBh6TmvBaw)\n- **zachary c. lipton: deep learning under distribution shift**\n  - [[Paper]](https://www.youtube.com/watch?v=WhpZKIra-FQ)\n- **preventing dataset shift from breaking machine-learning ...**\n  - [[Paper]](https://hal.archives-ouvertes.fr/hal-03293375/file/main.pdf)\n- **adapting event extractors to medical data - acl anthology**\n  - [[Paper]](https://aclanthology.org/2021.eacl-main.258.pdf)\n- **distinguishing clinical sentiment: the importance of domain ...**\n  - [[Paper]](https://aclanthology.org/W19-1915.pdf)\n- **the performance differences of a medical code prediction ...**\n  - [[Paper]](https://aclanthology.org/2022.clinicalnlp-1.10.pdf)\n- **investigating the challenges of temporal relation extraction ...**\n  - 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[[Paper]](https://arxiv.org/abs/2208.03609)\n- **generalizable and robust deep learning algorithm for atrial fibrillation diagnosis across ethnicities, ages and sexes**\n  - [[Paper]](https://arxiv.org/abs/2207.09667)\n- **domain-adaptiv","projects_url":"https://awesome.ecosyste.ms/api/v1/lists/monk1337%2Fawesome-robust-machine-learning/projects"}