ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar

    Deep Learning

  1. 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
  2. J. Yoon, J. Jordon, M. van der Schaar, "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019. [Link]
  3. 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]
  4. D. Jarrett, J. Yoon, and M. van der Schaar, "MATCH-Net: Dynamic Prediction in Survival Analysis using Convolutional Neural Networks," NIPS Machine Learning for Health Workshop 2018. - Selected as spotlight talk [Link] [Poster]
  5. C. Rietschel, J. Yoon, and M. van der Schaar, "Feature Selection for Survival Analysis with Competing Risks using Deep Learning," NIPS Machine Learning for Health Workshop 2018. [Link]
  6. C. Lee, N. Mastronarde, and M. van der Schaar, "Estimation of Individual Treatment Effect in Latent Confounder Models via Adversarial Learning," NIPS Machine Learning for Health Workshop 2018. - Selected as spotlight talk [Link] [Poster]
  7. T. Kyono, F. J. Gilbert, and M. van der Schaar, "MAMMO: A Deep Learning Solution for Facilitating Radiologist-Machine Collaboration in Breast Cancer Diagnosis," 2018. [Link]
  8. J. Yoon, W. R. Zame and M. van der Schaar, "Estimating Missing Data in Temporal Data Streams Using Multi-directional Recurrent Neural Networks," IEEE Transactions on Biomedical Engineering, 2018. [Link]
  9. F. Imrie, A. Bradley, M. van der Schaar, C. Deane, "Protein Family-specific Models using Deep Neural Networks and Transfer Learning Improve Virtual Screening and Highlight the Need for More Data," Journal of Chemical Information and Modeling, 2018.
  10. B. Lim, A. Alaa, M. van der Schaar, "Forecasting Treatment Responses Over Time Using Recurrent Marginal Structural Networks," NIPS, 2018. [Link]
  11. E. Giunchiglia, A. Nemchenko, M. van der Schaar, "RNN-SURV: a Deep Recurrent Model for Survival Analysis," International Conference on Artificial Neural Networks (ICANN), 2018. [Link]
  12. A. Nemchenko, T. Kyono, M. van der Schaar, "Siamese Survival Analysis with Competing Risks," International Conference on Artificial Neural Networks (ICANN), 2018. [Link]
  13. B. Lim, M. van der Schaar, "Disease-Atlas: Navigating Disease Trajectories using Deep Learning," Machine Learning for Healthcare Conference (MLHC), 2018. [Link] [Presentation] - Best Paper Award in IJCAI-BOOM Workshop
  14. J. Jordon, J. Yoon, M. van der Schaar, "Measuring the quality of Synthetic data for use in competitions," 2018 KDD Workshop on Machine Learning for Medicine and Healthcare, 2018. [Link]
  15. B. Lim, M. van der Schaar, ";Forecasting Disease Trajectories in Alzheimer's Disease Using Deep Learning," 2018 KDD Workshop on Machine Learning for Medicine and Healthcare, 2018. [Link]
  16. O. Atan, W. R. Zame, M. van der Schaar, "Counterfactual Policy Optimization Using Domain-Adversarial Neural Networks," ICML 2018 Causal Machine Learning Workshop, 2018. [Link]
  17. J. Yoon, J. Jordon, M. van der Schaar, "GAIN: Missing Data Imputation using Generative Adversarial Nets," ICML, 2018. [Link] [Appendix]
  18. 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]
  19. J. Yoon, J. Jordon, M. van der Schaar, "GANITE: Estimation of Individualized Treatment Effects using Generative Adversarial Nets," ICLR, 2018. [Link]
  20. J. Yoon, W. R. Zame, M. van der Schaar, "Deep Sensing: Active Sensing using Multi-directional Recurrent Neural Networks," ICLR, 2018. [Link]
  21. 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]
  22. O. Atan, J. Jordon, M. van der Schaar, "Deep-Treat: Learning Optimal Personalized Treatments from Observational Data using Neural Networks," AAAI, 2018. [Link]
  23. J. Yoon, M. van der Schaar, "E-RNN: Entangled Recurrent Neural Networks for Causal Prediction," ICML 2017 - Workshop on Principled Approaches to Deep Learning., 2017. [Link]
  24. A. M. Alaa, M. Weisz, M. van der Schaar, "Deep Counterfactual Networks with Propensity-Dropout," ICML 2017 - Workshop on Principled Approaches to Deep Learning., 2017. [Link]
  25. J. Yoon, W. R. Zame, M. van der Schaar, "Multi-directional Recurrent Neural Networks: A Novel Method for Estimating Missing Data," ICML 2017 - Time Series Workshop., 2017. [Link]