CHIL 2020 : ACM Conference on Health, Inference, and Learning

  in Conferences   Posted on November 22, 2019

Conference Information

Submission Deadline Monday 13 Jan 2020 Proceedings indexed by :
Conference Dates Apr 2, 2020 - Apr 4, 2020
Conference Address Toronto, Canada
Conference & Submission Link https://www.chilconference.org/
Conference Organizers : ( Deadline extended ? Click here to edit )

Conference Call for Papers

There are 4 tracks:

Track 1: Machine Learning

Track 2 Applications: Investigation, Evaluation, and Interpretation

Track 3 Policy: Impact, Economics, and Society

Track 4: Practice

Advances in machine learning are critical for a better understanding of health. Track 1 Machine Learning 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.

The goal of Track 2 Applications is to highlight works applying robust methods, models, or practices to identify, characterize, audit, evaluate, or benchmark systems. Whereas the goal of Track 1 is to select papers that show significant technical novelty, submit your work here if the contribution is more focused on solving a carefully motivated problem grounded in applications.

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