Sebastien Bubeck leads the Machine Learning Foundations group at Microsoft Research Redmond. He joined MSR in 2014, after three years as an assistant professor at Princeton University. He received several best paper awards at machine learning conferences for his work on online decision making, convex optimization, and adversarial robustness (NeurIPS 2021, NeurIPS 2018, ALT 2018, COLT 2016, COLT 2009). He also wrote two monographs, “Regret Analysis of Stochastic and Non-Stochastic Multi-Armed Bandit Problems” (2012) and “Convex Optimization: Algorithms and Complexity” (2014).
Podcast Episode 136 | March 23, 2023 - This episode features Sébastien Bubeck, who leads the Machine Learning Foundations group at Microsoft Research in Redmond. He and his collaborators conducted an extensive evaluation of GPT-4 while it was in development, and have published their findings in a paper that explores its capabilities and limitations—noting that it shows “sparks” of artificial general intelligence.
Podcast Episode 53 | December 5, 2018 - Dr. Sébastien Bubeck explains the difficulty of the multi-armed bandit problem in the context of a parameter- and data-rich online world. He also discusses a host of topics from randomness and convex optimization to metrical task systems and log n competitiveness to the surprising connection between Gaussian kernels and what he calls some of the most beautiful objects in mathematics.