ISyE Seminar Series: Daniel Russo
 "Adaptivity and Confounding in Multi-armed Bandit Experiments"Presentation by Daniel Russo Wednesday, April 20 |
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About the seminar: This talk explores a new model of bandit experiments where a potentially nonstationary sequence of contexts influences arms’ performance. Context-unaware algorithms risk confounding while those that perform correct inference face information delays. Our main insight is that an algorithm we call deconfounted Thompson sampling strikes a delicate balance between adaptivity and robustness. Its adaptivity leads to optimal efficiency properties in easy stationary instances, but it displays surprising resilience in hard nonstationary ones which cause other adaptive algorithms to fail. “Adaptivity and Confounding in Multi-Armed Bandit Experiments” (pdf) |
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Bio:
Daniel Russo is an associate professor in the Decision, Risk, and Operations division of the Columbia Business School. His research lies at the intersection of statistical machine learning and online decision making, mostly falling under the broad umbrella of reinforcement learning. Outside academia, Russo works with Spotify to apply reinforcement learning style models to audio recommendations. Daniel completed his undergraduate studies at the University of Michigan (2011) and his PhD at Stanford University (2015). He joined Columbia after spending one year as a postdoctoral researcher at Microsoft Research and one year as an assistant professor at the Northwestern’s Kellogg School of Management.
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