ACM Conference on Health, Inference, and Learning (CHIL 2020)

April 2-April4, 2020 ,Toronto, Canada

Scope & Topics

The ACM Conference on Health, Inference, and Learning (CHIL 2020) The ACM Conference on Health, Inference, and Learning (CHIL) solicits work across a variety of disciplines, including machine learning, statistics, epidemiology, health policy, operations, and economics. CHIL 2020 invites submissions touching on topics focused on relevant problems affecting health..

Topics of interest include, but are not limited to, the following

• Machine Learning: Models, Algorithms, Inference, and Estimation
• Applications: Investigation, Evaluation, and Interpretation
• Policy: Impact, Economics, and Society
• Practice: Deployments, Systems, and Datasets
• 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

Submission of Papers

Specifically, authors are invited to submit 8-10 page papers (with unlimited pages for references) to each of the tracks described below. To ensure that all submissions to CHIL are reviewed by a knowledgeable and appropriate set of reviewers, the conference is divided into tracks and areas of interest. Authors will select exactly one primary track and area of interest when they register their submissions, in addition to one or more sub-disciplines.

Important Dates