Workshop on Information-Theoretic Methods for Trustworthy Machine Learning
Workshop on Information-Theoretic Methods for Trustworthy Machine Learning
The Simons Institute will host a workshop on information-theoretic methods for trustworthy machine learning, May 22-25. Online participation is open to the public; in-person attendance by invitation only (email the organizers if interested).
The Simons Institute for the theory of computing will host a workshop exploring the information-theoretic foundations to make machine learning learning secure, reliable, robust, fair, and private. The workshop will include invited talks by experts from both academy and industry, student poster presentations, and time for fruitful discussions. Keynote talks will be given by Tara Javidi, Ilya Mironov, Todd Coleman, and Ayfer Ozgur. Online live-streaming via YouTube is open to the public; in-person attendance by invitation only (if interested, email the organizers at [email protected], [email protected], [email protected]).
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2023-05-22 00:00:00
2023-05-25 00:00:00
Workshop on Information-Theoretic Methods for Trustworthy Machine Learning
The Simons Institute will host a workshop on information-theoretic methods for trustworthy machine learning, May 22-25. Online participation is open to the public; in-person attendance by invitation only (email the organizers if interested).
Simons Institute for the Theory of Computing, Berkeley, CA
America/New_York
public
Event location
Simons Institute for the Theory of Computing, Berkeley, CA
Event type
Hybrid
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