Sebastien Bubeck

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Yin Tat LeePaul G. Allen School of Computer Science & Engineering, University of WashingtonVerified email at uw.edu
Ronen EldanWeizmann InstituteVerified email at weizmann.ac.il
Yuanzhi LiAssistant Professor at CMUVerified email at andrew.cmu.edu
Rémi MunosDeepMindVerified email at inria.fr
Gabor LugosiICREA and Universitat Pompeu FabraVerified email at upf.edu
Nicolò Cesa-BianchiProfessor of Computer Science, Università degli Studi di Milano and Politecnico di MilanoVerified email at unimi.it
Mark SellkeAssistant Professor of Statistics, Harvard UniversityVerified email at fas.harvard.edu
Yi ZhangSenior Researcher at Microsoft Research RedmondVerified email at microsoft.com
Eric HorvitzMicrosoft ResearchVerified email at microsoft.com
Hamid PalangiMicrosoft Research and University of WashingtonVerified email at microsoft.com
Marco Tulio RibeiroGoogle DeepMindVerified email at cs.washington.edu
Harsha NoriMicrosoft ResearchVerified email at microsoft.com
Scott LundbergGoogle DeepMindVerified email at google.com
Ece KamarMicrosoft ResearchVerified email at microsoft.com
Varun ChandrasekaranUniversity of Illinois Urbana-ChampaignVerified email at illinois.edu
Laurent MassoulieInria, MSR-Inria Joint CentreVerified email at inria.fr
Francis BachInria - Ecole Normale SupérieureVerified email at inria.fr
Csaba SzepesvariDeepMind & University of AlbertaVerified email at cs.ualberta.ca
Ilya RazenshteynCipherMode LabsVerified email at mit.edu
Kevin ScamanResearch scientist, INRIAVerified email at inria.fr

Sebastien Bubeck

Sr Principal Research Manager, ML Foundations group, Microsoft Research

Verified email at microsoft.com - Homepage

machine learning theoretical computer science convex optimization multi-armed bandits online algorithms


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Regret analysis of stochastic and nonstochastic multi-armed bandit problems S Bubeck, N Cesa-Bianchi Foundations and Trends in Machine Learning 5, 1-122, 2012	2953	2012
Convex optimization: Algorithms and complexity S Bubeck Foundations and Trends in Machine Learning 8, 231-357, 2014	2171	2014
Sparks of artificial general intelligence: Early experiments with gpt-4 S Bubeck, V Chandrasekaran, R Eldan, J Gehrke, E Horvitz, E Kamar, ... arXiv preprint arXiv:2303.12712, 2023	1123	2023
Best arm identification in multi-armed bandits JY Audibert, S Bubeck, R Munos COLT 2010, 2010	854	2010
Is Q-learning provably efficient? C Jin, Z Allen-Zhu, S Bubeck, MI Jordan Advances in neural information processing systems 31, 2018	825	2018
Pure exploration in multi-armed bandits problems S Bubeck, R Munos, G Stoltz Algorithmic Learning Theory, 23-37, 2009	568	2009
X-armed bandits S Bubeck, R Munos, G Stoltz, C Szepesvári Journal of Machine Learning Research 12, 1587-1627, 2011	485	2011
Minimax policies for adversarial and stochastic bandits JY Audibert, S Bubeck COLT 2009, 2009	472	2009
Provably robust deep learning via adversarially trained smoothed classifiers H Salman, J Li, I Razenshteyn, P Zhang, H Zhang, S Bubeck, G Yang Advances in Neural Information Processing Systems 32, 2019	467	2019
lil'UCB: An Optimal Exploration Algorithm for Multi-Armed Bandits K Jamieson, M Malloy, R Nowak, S Bubeck COLT 2014, 2013	435	2013
Benefits, limits, and risks of GPT-4 as an AI chatbot for medicine P Lee, S Bubeck, J Petro New England Journal of Medicine 388 (13), 1233-1239, 2023	405	2023
Optimal algorithms for smooth and strongly convex distributed optimization in networks K Scaman, F Bach, S Bubeck, YT Lee, L Massoulié international conference on machine learning, 3027-3036, 2017	318	2017
Pure exploration in finitely-armed and continuous-armed bandits S Bubeck, R Munos, G Stoltz Theoretical Computer Science 412, 1832-1852, 2010	299	2010
Bandits with heavy tail S Bubeck, N Cesa-Bianchi, G Lugosi IEEE Transactions on Information Theory 59 (11), 7711-7717, 2013	280	2013
Regret bounds and minimax policies under partial monitoring JY Audibert, S Bubeck The Journal of Machine Learning Research 11, 2635-2686, 2010	262	2010
Online Optimization in X-Armed Bandits S Bubeck, G Stoltz, C Szepesvári, R Munos Advances in Neural Information Processing Systems 21, 201-208, 2008	258	2008
Regret in online combinatorial optimization JY Audibert, S Bubeck, G Lugosi Mathematics of Operations Research 39 (1), 31-45, 2014	242	2014
The best of both worlds: Stochastic and adversarial bandits S Bubeck, A Slivkins COLT 2012, 2012	232	2012
Multiple identifications in multi-armed bandits S Bubeck, T Wang, N Viswanathan ICML 2012, 2012	226	2012
Adversarial examples from computational constraints S Bubeck, YT Lee, E Price, I Razenshteyn International Conference on Machine Learning, 831-840, 2019	221	2019

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