CFP Track 1: Machine Learning

CHIL CFP Track 1, Machine Learning: Models, Algorithms, Inference, and Estimation

Track Chairs: Dr. Shakir Mohamed, Dr. Sanmi Koyejo, Dr. Adrian Dalca, Dr. Uri Shalit, Irene Chen


Advances in machine learning are critical for a better understanding of health. This track seeks contributions in modeling, inference, and estimation in health-focused or health-inspired settings. We welcome submissions that develop novel methods and algorithms, introduce relevant machine learning tasks, or identify challenges with prevalent approaches. Submissions focused more on health applications, for example establishing baselines or suggesting new evaluation metrics for assessing algorithmic advances are encouraged to submit to Track 2 instead.

While submissions should address problems relevant to health, the contributions themselves are not required to be directly applied to health. For example, authors may use synthetic datasets and experiments to demonstrate the properties of algorithms. 

Authors may consider one or more machine learning sub-discipline(s) from the following list:

  • Bayesian learning
  • Causal inference
  • Computer vision
  • Deep learning architectures
  • Evaluation methods.
  • Inference
  • Knowledge graphs
  • Natural language processing
  • Reinforcement Learning
  • Representation learning
  • Robust learning
  • Structured learning
  • Supervised learning
  • Survival analysis
  • Time series
  • Transfer learning
  • Unsupervised learning
  • Explainability
  • Algorithmic Fairness

Authors may also consider sub-disciplines not listed here.

Upon submission, authors will select one or more relevant sub-discipline(s). Peer reviewers for a paper will be experts in the sub-discipline(s) selected upon its submission, so please select your relevant disciplines judiciously


Shalit, Uri, Fredrik D. Johansson, and David Sontag. “Estimating individual treatment effect: generalization bounds and algorithms.” Proceedings of the 34th International Conference on Machine Learning-Volume 70. JMLR. org, 2017.

Choi, Edward, et al. “MiME: Multilevel medical embedding of electronic health records for predictive healthcare.” Advances in Neural Information Processing Systems. 2018.

McDermott, Matthew BA, et al. “Semi-supervised biomedical translation with cycle Wasserstein regression GANs.” Thirty-Second AAAI Conference on Artificial Intelligence. 2018.

Futoma, Joseph, Sanjay Hariharan, and Katherine Heller. “Learning to detect sepsis with a multitask Gaussian process RNN classifier.” Proceedings of the 34th International Conference on Machine Learning-Volume 70., 2017.