261 citations · 1.4k across the 45 of their papers we have counts for
16 papers · 1 filter
Learning threshold neurons via the "edge of stability"
Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi +3
Existing analyses of neural network training often operate under the unrealistic assumption of an extremely small learning rate. This lies in stark contrast to practical wisdom and…
Online Learning for Active Cache Synchronization
Andrey Kolobov, Sébastien Bubeck, Julian Zimmert
Existing multi-armed bandit (MAB) models make two implicit assumptions: an arm generates a payoff only when it is played, and the agent observes every payoff that is generated. Thi…
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers
Hadi Salman, Greg Yang, Jerry Li +4
Recent works have shown the effectiveness of randomized smoothing as a scalable technique for building neural network-based classifiers that are provably robust to -norm ad…
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without
Sébastien Bubeck, Yuanzhi Li, Yuval Peres +1
We consider the non-stochastic version of the (cooperative) multi-player multi-armed bandit problem. The model assumes no communication at all between the players, and furthermore…
First-Order Bayesian Regret Analysis of Thompson Sampling
Sébastien Bubeck, Mark Sellke
We address online combinatorial optimization when the player has a prior over the adversary's sequence of losses. In this framework, Russo and Van Roy proposed an information-theor…
Improved Path-length Regret Bounds for Bandits
Sébastien Bubeck, Yuanzhi Li, Haipeng Luo +1
We study adaptive regret bounds in terms of the variation of the losses (the so-called path-length bounds) for both multi-armed bandit and more generally linear bandit. We first sh…