Stochastic Bandits: Foundations and Current Perspectives

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United States Simons Institute for the Theory of Computing
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Published on ● Video Link: https://www.youtube.com/watch?v=tlJqtrVYTuo


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Shipra Agrawal (Columbia University)
https://simons.berkeley.edu/talks/stochastic-bandits-foundations-and-current-perspectives-0
Data-Driven Decision Processes Boot Camp

This talk will focus on the main algorithms for stochastic bandits, a fundamental model for sequential learning that assumes that rewards of different actions come identically and independently from fixed distributions. We will cover the main algorithms for stochastic bandits (Upper Confidence Bound and Thompson Sampling) and subsequently discuss how they can be adapted to incorporate various additional constraints.



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Tags:
Simons Institute
theoretical computer science
UC Berkeley
Computer Science
Theory of Computation
Theory of Computing
Data-Driven Decision Processes Boot Camp
Shipra Agrawal