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van der Schaar Lab\n__Note__ : For the most recent papers and code, checkout https://github.com/vanderschaarlab.\n\n__Legacy code__ : This repository contains the implementations of algorithms developed\nby the [van der Schaar Lab](https://www.vanderschaar-lab.com/) for papers before 2023.\n\n\n\n## Content\nAn overview of the content of this repository is as below:\n```python\n.\n├── alg/        # Directory contains algorithms.\n├── app/        # Directory contains apps.\n├── cfg/        # Directory contains common config.\n├── doc/        # Directory contains common docs.\n├── init/       # Directory contains algorithms.\n├── template/   # Directory contains templates.\n└── util/       # Directory contains common utilities.\n```\n\n## Publications\nThe publications and the corresponding locations in the repo are listed below:\n\nPaper [[Link]](#) | Journal/Conference | Code\n--- | --- | ---\nBayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes [[Link]](https://proceedings.neurips.cc/paper/2017/hash/6a508a60aa3bf9510ea6acb021c94b48-Abstract.html) | NIPS 2017 | [alg/causal_multitask_gaussian_processes_ite](alg/causal_multitask_gaussian_processes_ite)\nDeep Multi-task Gaussian Processes for Survival Analysis with Competing Risks [[Link]](https://proceedings.neurips.cc/paper/2017/hash/861dc9bd7f4e7dd3cccd534d0ae2a2e9-Abstract.html) | NIPS 2017 | [alg/dgp_survival](alg/dgp_survival)\nAutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning [[Link]](https://icml.cc/Conferences/2018/Schedule?showEvent=2050) | ICML 2018 | [alg/autoprognosis](alg/autoprognosis)\nLimits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design [[Link]](http://proceedings.mlr.press/v80/alaa18a.html) | ICML 2018 | [alg/causal_multitask_gaussian_processes_ite](alg/causal_multitask_gaussian_processes_ite)\nGAIN: Missing Data Imputation using Generative Adversarial Nets [[Link]](http://proceedings.mlr.press/v80/yoon18a.html) | ICML 2018 | [alg/gain](alg/gain)\nRadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks [[Link]](http://proceedings.mlr.press/v80/yoon18b.html) | ICML 2018 | [alg/RadialGAN](alg/RadialGAN)\nGANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets [[Link]](https://openreview.net/forum?id=ByKWUeWA-) | ICLR 2018 | [alg/ganite](alg/ganite)\nDeep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks [[Link]](https://openreview.net/forum?id=r1SnX5xCb) | ICLR 2018 | [alg/DeepSensing (MRNN)](alg/DeepSensing%20(MRNN))\nDeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks [[Link]](https://aaai.org/ocs/index.php/AAAI/AAAI18/paper/view/16160/15945) | AAAI 2018 | [alg/deephit](alg/deephit)\nINVASE: Instance-wise Variable Selection using Neural Networks [[Link]](https://openreview.net/forum?id=BJg_roAcK7) | ICLR 2019 | [alg/invase](alg/invase)\nPATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees [[Link]](https://openreview.net/forum?id=S1zk9iRqF7) | ICLR 2019 | [alg/pategan](alg/pategan)\nKnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks [[Link]](https://openreview.net/forum?id=ByeZ5jC5YQ) | ICLR 2019 | [alg/knockoffgan](alg/knockoffgan)\nASAC: Active Sensing using Actor-Critic Models [[Link]](https://arxiv.org/abs/1906.06796) | MLHC 2019 | [alg/asac](alg/asac)\nDemystifying Black-box Models with Symbolic Metamodels [[Link]](https://papers.nips.cc/paper/2019/hash/567b8f5f423af15818a068235807edc0-Abstract.html) | NeurIPS 2019 | [alg/symbolic_metamodeling](alg/symbolic_metamodeling)\nDifferentially Private Bagging: Improved Utility and Cheaper Privacy than Subsample-and-Aggregate [[Link]](https://papers.nips.cc/paper/2019/hash/5dec707028b05bcbd3a1db5640f842c5-Abstract.html) | NeurIPS 2019 | [alg/dpbag](alg/dpbag)\nTime-series Generative Adversarial Networks [[Link]](https://papers.nips.cc/paper/2019/hash/c9efe5f26cd17ba6216bbe2a7d26d490-Abstract.html) | NeurIPS 2019 | [alg/timegan](alg/timegan)\nAttentive State-Space Modeling of Disease Progression [[Link]](https://papers.nips.cc/paper/2019/hash/1d0932d7f57ce74d9d9931a2c6db8a06-Abstract.html) | NeurIPS 2019 | [alg/attentivess](alg/attentivess)\nConditional Independence Testing using Generative Adversarial Networks [[Link]](https://arxiv.org/abs/1907.04068) | NeurIPS 2019 | [alg/gcit](alg/gcit)\nDynamic-DeepHit: A Deep Learning Approach for Dynamic Survival Analysis with Competing Risks based on Longitudinal Data [[Link]](https://ieeexplore.ieee.org/document/8681104) | IEEE | [alg/dynamic_deephit](alg/dynamic_deephit)\nTemporal Quilting for Survival Analysis [[Link]](http://proceedings.mlr.press/v89/lee19a.html) | AISTATS 2019 | [alg/survivalquilts](alg/survivalquilts)\nEstimating Counterfactual Treatment Outcomes over Time through Adversarially Balanced Representations [[Link]](https://openreview.net/forum?id=BJg866NFvB) | ICLR 2020 | [alg/counterfactual_recurrent_network](alg/counterfactual_recurrent_network)\nContextual Constrained Learning for Dose-Finding Clinical Trials [[Link]](https://arxiv.org/abs/2001.02463) | AISTATS 2020 | [alg/c3t_budgets](alg/c3t_budgets)\nLearning Overlapping Representations for the Estimation of Individualized Treatment Effects [[Link]](https://arxiv.org/abs/2001.04754) | AISTATS 2020 | [alg/dklite](alg/dklite)\nLearning Dynamic and Personalized Comorbidity Networks from Event Data using Deep Diffusion Processes [[Link]](https://arxiv.org/abs/2001.02585) | AISTATS 2020 | [alg/dynamic_disease_network_ddp](alg/dynamic_disease_network_ddp)\nStepwise Model Selection for Sequence Prediction via Deep Kernel Learning [[Link]](https://arxiv.org/abs/2001.03898) | AISTATS 2020 | [alg/smsdkl](alg/smsdkl)\nTemporal Phenotyping using Deep Predicting Clustering of Disease Progression [[Link]](http://proceedings.mlr.press/v119/lee20h.html) | ICML 2020 | [alg/ac_tpc](alg/ac_tpc)\nTime Series Deconfounder: Estimating Treatment Effects over Time in the Presence of Hidden Confounders [[Link]](http://proceedings.mlr.press/v119/bica20a.html) | ICML 2020 | [alg/time_series_deconfounder](alg/time_series_deconfounder)\nDiscriminative Jackknife: Quantifying Uncertainty in Deep Learning via Higher-Order Influence Functions [[Link]](http://proceedings.mlr.press/v119/alaa20a.html) | ICML 2020 | [alg/discriminative-jackknife](alg/discriminative-jackknife)\nFrequentist Uncertainty in Recurrent Neural Networks via Blockwise Influence Functions [[Link]](http://proceedings.mlr.press/v119/alaa20b.html) | ICML 2020 | [alg/rnn-blockwise-jackknife](alg/rnn-blockwise-jackknife)\nUnlabelled Data Improves Bayesian Uncertainty Calibration under Covariate Shift [[Link]](http://proceedings.mlr.press/v119/chan20a.html) | ICML 2020 | [alg/transductive_dropout](alg/transductive_dropout)\nAnonymization Through Data Synthesis Using Generative Adversarial Networks (ADS-GAN) [[Link]](https://ieeexplore.ieee.org/document/9034117) | IEEE | [alg/adsgan](alg/adsgan)\nWhen and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes [[Link]](https://vanderschaar-lab.com/papers/NeurIPS2020_CGP.pdf) | NeurIPS 2020 | [alg/compartmental_gp](alg/compartmental_gp)\nStrictly Batch Imitation Learning by Energy-based Distribution Matching [[Link]](https://arxiv.org/abs/2006.14154) | NeurIPS 2020 | [alg/edm](alg/edm)\nGradient Regularized V-Learning for Dynamic Treatment Regimes [[Link]](https://vanderschaar-lab.com/papers/NeurIPS2020_GRV.pdf) | NeurIPS 2020 | [alg/grv](alg/grv)\nCASTLE: Regularization via Auxiliary Causal Graph Discovery [[Link]](https://arxiv.org/abs/2009.13180) | NeurIPS 2020 | [alg/castle](alg/castle)\nOrganITE: Optimal transplant donor organ offering using an individual treatment effect [[Link]](https://vanderschaar-lab.com/papers/NeurIPS2020_OrganITE.pdf) | NeurIPS 2020 | [alg/organite](alg/organite)\nRobust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification [[Link]](https://arxiv.org/abs/2006.07917) | NeurIPS 2020 | [alg/r2p-hte](alg/r2p-hte)\nEstimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks [[Link]](https://arxiv.org/abs/2002.12326) | NeurIPS 2020 | [alg/scigan](alg/scigan)\nLearning outside the Black-Box: The pursuit of interpretable models [[Link]](https://arxiv.org/abs/2011.08596) | NeurIPS 2020 | [alg/Symbolic-Pursuit](alg/Symbolic-Pursuit)\nVIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain [[Link]](https://papers.nips.cc/paper/2020/hash/7d97667a3e056acab9aaf653807b4a03-Abstract.html) | NeurIPS 2020 | [alg/vime](alg/vime)\nScalable Bayesian Inverse Reinforcement Learning [[Link]](https://openreview.net/pdf?id=4qR3coiNaIv) | ICLR 2021 | [alg/scalable-birl](alg/scalable-birl)\nNonparametric Estimation of Heterogeneous Treatment Effects: From Theory to Learning Algorithms [[Link]](https://arxiv.org/abs/2101.10943) | AISTATS 2021 | [alg/CATENets](https://github.com/vanderschaarlab/CATENets)\nLearning Matching Representations for Individualized Organ Transplantation Allocation [[Link]](https://arxiv.org/abs/2101.11769) | AISTATS 2021| [alg/MatchingRep](alg/MatchingRep)\nExplaining by Imitating: Understanding Decisions by Interpretable Policy Learning [[Link]](https://openreview.net/forum?id=unI5ucw_Jk) | ICLR 2021 | [alg/interpole](alg/interpole)\nInverse Decision Modeling: Learning Interpretable Representations of Behavior [[Link]](http://proceedings.mlr.press/v139/jarrett21a.html) | ICML 2021 | [alg/ibrc](alg/ibrc)\nPolicy Analysis using Synthetic Controls in Continuous-Time [[Link]](http://proceedings.mlr.press/v139/bellot21a/bellot21a.pdf) | ICML 2021 | [alg/Synthetic-Controls-in-Continuous-Time](https://github.com/vanderschaarlab/Synthetic-Controls-in-Continuous-Time/)\nLearning Queueing Policies for Organ Transplantation Allocation using Interpretable Counterfactual Survival Analysis [[Link]](http://proceedings.mlr.press/v139/berrevoets21a/berrevoets21a.pdf) | ICML 2021 | [alg/organsync](https://github.com/vanderschaarlab/organsync/)\nExplaining Time Series Predictions with Dynamic Masks [[Link]](http://proceedings.mlr.press/v139/crabbe21a.html) | ICML 2021 | [alg/Dynamask](https://github.com/vanderschaarlab/Dynamask/)\nGenerative Time-series Modeling with Fourier Flows [[Link]](https://openreview.net/forum?id=PpshD0AXfA) | ICLR 2021 | [alg/Fourier-flows](https://github.com/vanderschaarlab/Fourier-flows/)\nOn Inductive Biases for Heterogeneous Treatment Effect Estimation [[Link]](https://arxiv.org/pdf/2106.03765.pdf) | NeurIPS 2021 | [alg/CATENets](https://github.com/vanderschaarlab/CATENets/)\nReally Doing Great at Estimating CATE? A Critical Look at ML Benchmarking Practices in Treatment Effect Estimation [[Link]](https://openreview.net/pdf?id=FQLzQqGEAH) | NeurIPS 2021 | [alg/CATENets](https://github.com/vanderschaarlab/CATENets/)\nThe Medkit-Learn(ing) Environment: Medical Decision Modelling through Simulation [[Link]](https://arxiv.org/abs/2106.04240) | NeurIPS 2021 | [alg/medkit-learn](https://github.com/vanderschaarlab/medkit-learn)\nMIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms [[Link]](https://neurips.cc/Conferences/2021/ScheduleMultitrack?event=27670) | NeurIPS 2021 | [alg/MIRACLE](https://github.com/vanderschaarlab/MIRACLE)\nDECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks [[Link]](https://neurips.cc/Conferences/2021/ScheduleMultitrack?event=27552) | NeurIPS 2021 | [alg/DECAF](https://github.com/vanderschaarlab/DECAF)\nExplaining Latent Representations with a Corpus of Examples [[Link]](https://arxiv.org/abs/2110.15355) | NeurIPS 2021 | [alg/Simplex](https://github.com/vanderschaarlab/Simplex)\nClosing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation [[Link]](https://neurips.cc/Conferences/2021/ScheduleMultitrack?event=26815) | NeurIPS 2021 | [alg/iTransplant](https://github.com/vanderschaarlab/iTransplant)\nIntegrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression [[Link]](https://papers.neurips.cc/paper/2021/hash/5ea1649a31336092c05438df996a3e59-Abstract.html) | NeurIPS 2021 | [alg/Hybrid-ODE-NeurIPS-2021](https://github.com/vanderschaarlab/Hybrid-ODE-NeurIPS-2021)\nSyncTwin: Treatment Effect Estimation with Longitudinal Outcomes [[Link]](https://proceedings.neurips.cc/paper/2021/hash/19485224d128528da1602ca47383f078-Abstract.html) | NeurIPS 2021 | [alg/SyncTwin-NeurIPS-2021](https://github.com/vanderschaarlab/SyncTwin-NeurIPS-2021)\nConformal Time-series Forecasting [[Link]](https://proceedings.neurips.cc/paper/2021/hash/312f1ba2a72318edaaa995a67835fad5-Abstract.html) | NeurIPS 2021 | [alg/conformal-rnn](https://github.com/vanderschaarlab/conformal-rnn/tree/master)\nEstimating Multi-cause Treatment Effects via Single-cause Perturbation [[Link]](https://proceedings.neurips.cc/paper/2021/hash/c793b3be8f18731f2a4c627fb3c6c63d-Abstract.html) | NeurIPS 2021 | [alg/Single-Cause-Perturbation-NeurIPS-2021](https://github.com/vanderschaarlab/Single-Cause-Perturbation-NeurIPS-2021/)\nInvariant Causal Imitation Learning for Generalizable Policies [[Link]](https://papers.nips.cc/paper/2021/file/204904e461002b28511d5880e1c36a0f-Paper.pdf) | NeurIPS 2021 | [alg/Invariant-Causal-Imitation-Learning](https://github.com/vanderschaarlab/Invariant-Causal-Imitation-Learning/tree/main)\nInferring Lexicographically-Ordered Rewards from Preferences [[Link]](https://ojs.aaai.org/index.php/AAAI/article/view/20516) | AAAI 2022 | [alg/lori](https://github.com/vanderschaarlab/lori)\nInverse Online Learning: Understanding Non-Stationary and Reactionary Policies [[Link]](https://openreview.net/forum?id=DYypjaRdph2) | ICLR 2022 | [alg/inverse-online](https://github.com/vanderschaarlab/inverse-online)\nD-CODE: Discovering Closed-form ODEs from Observed Trajectories [[Link]](https://openreview.net/forum?id=wENMvIsxNN) | ICLR 2022 | [alg/D-CODE-ICLR-2022](https://github.com/vanderschaarlab/D-CODE-ICLR-2022)\nNeural graphical modelling in continuous-time: consistency guarantees and algorithms [[Link]](https://openreview.net/forum?id=SsHBkfeRF9L) | ICLR 2022 | [alg/Graphical-modelling-continuous-time](https://github.com/vanderschaarlab/Graphical-modelling-continuous-time)\nLabel-Free Explainability for Unsupervised Models [[Link]](https://proceedings.mlr.press/v162/crabbe22a) | ICML 2022 | [alg/Label-Free-XAI](https://github.com/vanderschaarlab/Label-Free-XAI)\nInverse Contextual Bandits: Learning How Behavior Evolves over Time [[Link]](https://proceedings.mlr.press/v162/huyuk22a.html) | ICML 2022 | [alg/invconban](https://github.com/vanderschaarlab/invconban)\nData-SUITE: Data-centric identification of in-distribution incongruous examples [[Link]](https://proceedings.mlr.press/v162/seedat22a.html) | ICML 2022 | [alg/Data-SUITE](https://github.com/vanderschaarlab/Data-SUITE)\nContinuous-Time Modeling of Counterfactual Outcomes Using Neural Controlled Differential Equations [[Link]](https://proceedings.mlr.press/v162/seedat22b) | ICML 2022 | [alg/TE-CDE](https://github.com/vanderschaarlab/TE-CDE)\nConcept Activation Regions: A Generalized Framework For Concept-Based Explanations[[Link]](https://arxiv.org/abs/2209.11222) | NeurIPS 2022 | [alg/CARs](https://github.com/vanderschaarlab/CARs)\nBenchmarking Heterogeneous Treatment Effect Models through the Lens of Interpretability[[Link]](https://arxiv.org/abs/2206.08363) | NeurIPS 2022 | [alg/ITErpretability](https://github.com/vanderschaarlab/ITErpretability)\nTransfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation[[Link]](https://arxiv.org/abs/2210.06183) | NeurIPS 2022 | [alg/HTCE-learners](https://github.com/vanderschaarlab/HTCE-learners)\nSynthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning[[Link]](https://arxiv.org/abs/2210.05320) | NeurIPS 2022 | [alg/HTCE-learners](https://github.com/vanderschaarlab/synthetic-model-combination)\n\u003cbr/\u003e\n\nDetails of apps and other software is listed below:\n\nApp/Software [[Link]](#) | Description | Publication | Code\n--- | --- | --- | ---\nAdjutorium COVID-19 [[Link]](https://www.vanderschaar-lab.com/paper-on-covid-19-hospital-capacity-planning-published-in-machine-learning/) | Adjutorium COVID-19: an AI-powered tool that accurately predicts how COVID-19 will impact resource needs (ventilators, ICU beds, etc.) at the individual patient level and the hospital level | - | [app/adjutorium-covid19-public](app/adjutorium-covid19-public)\nClairvoyance [[Link]](https://www.vanderschaar-lab.com/clairvoyance-alpha-the-first-unified-end-to-end-automl-pipeline-for-time-series-data/) | Clairvoyance: A Pipeline Toolkit for Medical Time Series | [ICML 2021](https://openreview.net/forum?id=xnC8YwKUE3k) | [clairvoyance repository](https://github.com/vanderschaarlab/clairvoyance)\nClairvoyance2 [[Link]](https://github.com/vanderschaarlab/clairvoyance2) | `clairvoyance2`: a Unified Toolkit for Medical Time Series | - | [clairvoyance2 repository](https://github.com/vanderschaarlab/clairvoyance2)\nHide-and-Seek Privacy Challenge [[Link]](http://www.vanderschaar-lab.com/privacy-challenge/) | Hide-and-Seek Privacy Challenge: Synthetic Data Generation vs. Patient Re-identification with Clinical Time-series Data | [NeurIPS 2020 competition track](https://arxiv.org/abs/2007.12087) | [app/hide-and-seek](app/hide-and-seek)\n\n## Citations\nPlease cite the *the applicable papers* and [van der Schaar Lab repository](https://github.com/vanderschaarlab/mlforhealthlabpub/) if you use the software.\n\n## Breakdown by category\n\n**See breakdown [here](https://github.com/vanderschaarlab/.github/tree/main/profile).**\n\n## License\nCopyright **2019-2022** van der Schaar Lab.\n\nThis software is released under the [3-Clause BSD license](https://opensource.org/licenses/BSD-3-Clause) unless mentioned otherwise by the respective algorithms and apps.\n\n## Installation instructions\n*See individual algorithm and app directories for installation instructions.*\n\nSee also [doc/install.md](doc/install.md) for common installation instructions.\n\n## Tutorials and or examples\n*See individual algorithm and app directories for tutorials and examples.*\n\n## Data\nData files (as well as other large files such as saved models etc.) can be downloaded as per instructions in the `DATA-*.md` (see e.g. [DATA-PUBLIC.md](./DATA-PUBLIC.md)) files found in the corresponding directories.\n\n## More info\nFor more information on the van der Schaar Lab’s work, visit [our homepage](https://www.vanderschaar-lab.com/).\n\n## References\n*See individual algorithm and app directories for references.*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvanderschaarlab%2Fmlforhealthlabpub","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fvanderschaarlab%2Fmlforhealthlabpub","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fvanderschaarlab%2Fmlforhealthlabpub/lists"}