ML-AIM Machine Learning and Artificial Intelligence for Medicine

Research Laboratory led by Prof. Mihaela van der Schaar

    Interpretability and Explainability


  1. A. M. Alaa, M. van der Schaar, "Demystifying Black-box Models with Symbolic Metamodels," Neural Information Processing Systems (NeurIPS), 2019. [Link]
  2. A. M. Alaa, M. van der Schaar, "Attentive State-Space Modeling of Disease Progression," Neural Information Processing Systems (NeurIPS), 2019. [Link]
  3. K. Ahuja, W. Zame, M. van der Schaar, "Optimal Piecewise Approximations for Model Interpretations," Asilomar Conference on Signals, Systems, and Computers., 2019.
  4. J. Yoon, J. Jordon, M. van der Schaar, "INVASE: Instance-wise Variable Selection using Neural Networks," International Conference on Learning Representations (ICLR), 2019. [Link]
  5. O. Lahav, N. Mastronarde, and M. van der Schaar, "What is Interpretable? Using Machine Learning to Design Interpretable Decision-Support Systems," NIPS Machine Learning for Health Workshop 2018. - Selected as spotlight talk [Link] [Poster]