ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar
- All publications
- Recent NIPS, ICML, ICLR, AAAI, AISTATS conferences
- Clinical publications

    Recent NIPS, ICML, ICLR, AAAI, AISTATS conferences


  1. A. M. Alaa, M. van der Schaar, "Validating Causal Inference Models via Influence Functions," International Conference on Machine Learning (ICML), 2019.
  2. C. Lee, W. R. Zame, A. M. Alaa, M. van der Schaar, "Temporal Quilting for Survival Analysis," International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [Link] [Supplementary Materials]
  3. A. Bellot, M. van der Schaar, "Boosting Survival Predictions with Auxiliary Data from Heterogeneous Domains," International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [Link]
  4. O. Atan, W. R. Zame, M. van der Schaar, "Sequential Patient Recruitment and Allocation for Adaptive Clinical Trials," International Conference on Artificial Intelligence and Statistics (AISTATS), 2019. [Link]
  5. J. Jordon, J. Yoon, M. van der Schaar, "KnockoffGAN: Generating Knockoffs for Feature Selection using Generative Adversarial Networks," International Conference on Learning Representations (ICLR), 2019. [Link] - Selected as oral presentation
  6. J. Yoon, J. Jordon, M. van der Schaar, "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019. [Link]
  7. J. Yoon, J. Jordon, M. van der Schaar, "PATE-GAN: Generating Synthetic Data with Differential Privacy Guarantees," International Conference on Learning Representations (ICLR), 2019. [Link]
  8. A. Bellot, M. van der Schaar, "Multitask Boosting for Survival Analysis with Competing Risks," NIPS, 2018. [Link]
  9. B. Lim, A. Alaa, M. van der Schaar, "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks," NIPS, 2018. [Link]
  10. J. Yoon, J. Jordon, M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," ICML, 2018. [Link] [Appendix]
  11. J. Yoon, J. Jordon, M. van der Schaar, "RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks," ICML, 2018. [Link] [Appendix]
  12. A. M. Alaa, M. van der Schaar, "AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning," ICML, 2018. [Link] [Webpage]
  13. A. M. Alaa, M. van der Schaar, "Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design," ICML, 2018. [Link]
  14. J. Yoon, J. Jordon, M. van der Schaar, "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets," ICLR, 2018. [Link]
  15. J. Yoon, W. R. Zame, M. van der Schaar, "Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks," ICLR, 2018. [Link]
  16. A. Bellot, M. van der Schaar, "Tree-based Bayesian Mixture Model for Competing Risks," AISTATS, 2018. [Link]
  17. C. Lee, W. R. Zame, J. Yoon, M. van der Schaar, "DeepHit: A Deep Learning Approach to Survival Analysis with Competing Risks," AAAI, 2018. [Link] [Supplementary Materials]
  18. O. Atan, J. Jordon, M. van der Schaar, "Deep-Treat: Learning Optimal Personalized Treatments from Observational Data using Neural Networks," AAAI, 2018. [Link]
  19. A. M. Alaa, M. van der Schaar, "Deep Multi-task Gaussian Processes for Survival Analysis with Competing Risks," NIPS, 2017. [Link] - Selected as a spotlight paper
  20. A. M. Alaa, M. van der Schaar, "Bayesian Inference of Individualized Treatment Effects using Multi-task Gaussian Processes," NIPS, 2017. [Link] [Supplementary Materials]
  21. K. Ahuja, W. R. Zame, M. van der Schaar, "DPSCREEN: Dynamic Personalized Screening," NIPS, 2017. [Link] [Poster]
  22. A. M. Alaa, S. Hu, M. van der Schaar, "Learning from Clinical Judgments: Semi-Markov-Modulated Marked Hawkes Processes for Risk Prognosis," ICML, 2017. [Link]
  23. J. Yoon, A. M. Alaa, M. Cadeiras, M. van der Schaar, "Personalized Donor-Recipient Matching for Organ Transplantation," AAAI, 2017. [Link] [Poster]
  24. J. Xu, Y. Han, D. Marcu, M. van der Schaar, "Progressive Prediction of Student Performance in College Programs," AAAI, 2017. [Link]
  25. A. M. Alaa and M. van der Schaar, "Balancing Suspense and Surprise: Timely Decision Making with Endogenous Information Acquisition," NIPS, 2016. [Link] [Poster]
  26. W. Hoiles and M. van der Schaar, "A Non-parametric Learning Method for Confidently Estimating Patient's Clinical State and Dynamics ," NIPS, 2016. [Link] [Poster]
  27. J. Yoon, A. M. Alaa, S. Hu, M. van der Schaar, "ForecastICU: A Prognostic Decision Support System for Timely Prediction of Intensive Care Unit Admission ," ICML, 2016. [Link]
  28. W. Whoiles, M. van der Schaar, "Bounded Off-Policy Evaluation with Missing Data for Course Recommendation and Curriculum Design," ICML, 2016. [Link]
  29. E. Soltanmohammadi, M. Naraghi-Pour, M. van der Schaar, "Context-based Unsupervised Data Fusion for Decision Making," ICML, 2015. [Link]
  30. O. Atan, C. Tekin, M. van der Schaar, "Global Multi-armed Bandits with H?der Continuity," AISTATS, 2015. [Link]
  31. C. Tekin and M. van der Schaar, "Discovering, Learning and Exploiting Relevance," Neural Information Processing Systems (NIPS), 2014. [Link]