Consistent Minimization of Clustering Objective Functions. - Ulrike von Luxburg; Sébastien Bubeck; Stefanie Jegelka; Michael Kaufmann 001 (NIPS, 2007)

Online Optimization in X-Armed Bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (NIPS, 2008)

Pure Exploration for Multi-Armed Bandit Problems - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (CoRR, 2008)

Pure Exploration in Multi-armed Bandits Problems. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (ALT, 2009)

Minimax Policies for Adversarial and Stochastic Bandits. - Jean-Yves Audibert; Sébastien Bubeck (COLT, 2009)

Nearest Neighbor Clustering: A Baseline Method for Consistent Clustering with Arbitrary Objective Functions. - Sébastien Bubeck; Ulrike von Luxburg (J. Mach. Learn. Res., 2009)

Best Arm Identification in Multi-Armed Bandits. - Jean-Yves Audibert; Sébastien Bubeck; Rémi Munos (COLT, 2010)

Open Loop Optimistic Planning. - Sébastien Bubeck; Rémi Munos (COLT, 2010)

X-Armed Bandits - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (CoRR, 2010)

Regret Bounds and Minimax Policies under Partial Monitoring. - Jean-Yves Audibert; Sébastien Bubeck (J. Mach. Learn. Res., 2010)

Lipschitz Bandits without the Lipschitz Constant. - Sébastien Bubeck; Gilles Stoltz; Jia Yuan Yu (ALT, 2011)

Multi-Bandit Best Arm Identification. - Victor Gabillon; Mohammad Ghavamzadeh; Alessandro Lazaric; Sébastien Bubeck (NIPS, 2011)

Minimax Policies for Combinatorial Prediction Games. - Jean-Yves Audibert; Sébastien Bubeck; Gábor Lugosi (COLT, 2011)

X-Armed Bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz; Csaba Szepesvári (J. Mach. Learn. Res., 2011)

Pure exploration in finitely-armed and continuous-armed bandits. - Sébastien Bubeck; Rémi Munos; Gilles Stoltz (Theor. Comput. Sci., 2011)

Optimal discovery with probabilistic expert advice. - Sébastien Bubeck; Damien Ernst; Aurélien Garivier (CDC, 2012)

Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems. - Sébastien Bubeck; Nicolò Cesa-Bianchi (Found. Trends Mach. Learn., 2012)

Towards Minimax Policies for Online Linear Optimization with Bandit Feedback. - Sébastien Bubeck; Nicolò Cesa-Bianchi; Sham M. Kakade (COLT, 2012)

The Best of Both Worlds: Stochastic and Adversarial Bandits. - Sébastien Bubeck; Aleksandrs Slivkins (COLT, 2012)

Bounded regret in stochastic multi-armed bandits. - Sébastien Bubeck; Vianney Perchet; Philippe Rigollet (COLT, 2013)

Multiple Identifications in Multi-Armed Bandits. - Sébastien Bubeck; Tengyao Wang; Nitin Viswanathan (ICML (1), 2013)

Prior-free and prior-dependent regret bounds for Thompson Sampling. - Sébastien Bubeck; Che-Yu Liu (NIPS, 2013)

On Finding the Largest Mean Among Many. - Kevin G. Jamieson; Matthew Malloy; Robert D. Nowak; Sébastien Bubeck (CoRR, 2013)

A note on the Bayesian regret of Thompson Sampling with an arbitrary prior - Sébastien Bubeck; Che-Yu Liu (CoRR, 2013)

Optimal discovery with probabilistic expert advice: finite time analysis and macroscopic optimality. - Sébastien Bubeck; Damien Ernst; Aurélien Garivier (J. Mach. Learn. Res., 2013)

Bandits With Heavy Tail. - Sébastien Bubeck; Nicolò Cesa-Bianchi; Gábor Lugosi (IEEE Trans. Inf. Theory, 2013)

lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits. - Kevin G. Jamieson; Matthew Malloy; Robert D. Nowak; Sébastien Bubeck (COLT, 2014)

Most Correlated Arms Identification. - Che-Yu Liu; Sébastien Bubeck (COLT, 2014)

Theory of Convex Optimization for Machine Learning. - Sébastien Bubeck (CoRR, 2014)

From trees to seeds: on the inference of the seed from large trees in the uniform attachment model. - Sébastien Bubeck; Ronen Eldan; Elchanan Mossel; Miklós Z. Rácz (CoRR, 2014)

Regret in Online Combinatorial Optimization. - Jean-Yves Audibert; Sébastien Bubeck; Gábor Lugosi (Math. Oper. Res., 2014)

Bandit Convex Optimization: ( sqrt{T} ) Regret in One Dimension. - Sébastien Bubeck; Ofer Dekel; Tomer Koren; Yuval Peres (COLT, 2015)

The entropic barrier: a simple and optimal universal self-concordant barrier. - Sébastien Bubeck; Ronen Eldan (COLT, 2015)

Finite-Time Analysis of Projected Langevin Monte Carlo. - Sébastien Bubeck; Ronen Eldan; Joseph Lehec (NIPS, 2015)

Entropic CLT and phase transition in high-dimensional Wishart matrices. - Sébastien Bubeck; Shirshendu Ganguly (CoRR, 2015)

A geometric alternative to Nesterov's accelerated gradient descent. - Sébastien Bubeck; Yin Tat Lee; Mohit Singh (CoRR, 2015)

Convex Optimization: Algorithms and Complexity. - Sébastien Bubeck (Found. Trends Mach. Learn., 2015)

Exceptional rotations of random graphs: a VC theory. - Louigi Addario-Berry; Shankar Bhamidi; Sébastien Bubeck; Luc Devroye; Gábor Lugosi; Roberto Imbuzeiro Oliveira (J. Mach. Learn. Res., 2015)

On the Influence of the Seed Graph in the Preferential Attachment Model. - Sébastien Bubeck; Elchanan Mossel; Miklós Z. Rácz (IEEE Trans. Netw. Sci. Eng., 2015)

Multi-scale exploration of convex functions and bandit convex optimization. - Sébastien Bubeck; Ronen Eldan (COLT, 2016)

Black-box Optimization with a Politician. - Sébastien Bubeck; Yin Tat Lee (ICML, 2016)

Basic models and questions in statistical network analysis. - Miklós Z. Rácz; Sébastien Bubeck (CoRR, 2016)

On the Local Profiles of Trees. - Sébastien Bubeck; Nati Linial (J. Graph Theory, 2016)

Testing for high-dimensional geometry in random graphs. - Sébastien Bubeck; Jian Ding; Ronen Eldan; Miklós Z. Rácz (Random Struct. Algorithms, 2016)

Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. - Kevin Scaman; Francis R. Bach; Sébastien Bubeck; Yin Tat Lee; Laurent Massoulié (ICML, 2017)

Online Auctions and Multi-scale Online Learning. - Sébastien Bubeck; Nikhil R. Devanur; Zhiyi Huang 002; Rad Niazadeh (EC, 2017)

Local max-cut in smoothed polynomial time. - Omer Angel; Sébastien Bubeck; Yuval Peres; Fan Wei (STOC, 2017)

Finding Adam in random growing trees. - Sébastien Bubeck; Luc Devroye; Gábor Lugosi (Random Struct. Algorithms, 2017)

Sparsity, variance and curvature in multi-armed bandits. - Sébastien Bubeck; Michael B. Cohen; Yuanzhi Li (ALT, 2018)

Conference on Learning Theory 2018: Preface. - Sébastien Bubeck; Philippe Rigollet (COLT, 2018)

Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits. - Zeyuan Allen-Zhu; Sébastien Bubeck; Yuanzhi Li (ICML, 2018)

Is Q-Learning Provably Efficient? - Chi Jin 001; Zeyuan Allen-Zhu; Sébastien Bubeck; Michael I. Jordan (NeurIPS, 2018)

Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. - Kevin Scaman; Francis R. Bach; Sébastien Bubeck; Laurent Massoulié; Yin Tat Lee (NeurIPS, 2018)

An homotopy method for lp regression provably beyond self-concordance and in input-sparsity time. - Sébastien Bubeck; Michael B. Cohen; Yin Tat Lee; Yuanzhi Li (STOC, 2018)

k-server via multiscale entropic regularization. - Sébastien Bubeck; Michael B. Cohen; Yin Tat Lee; James R. Lee; Aleksander Madry (STOC, 2018)

Adversarial examples from computational constraints. - Sébastien Bubeck; Eric Price 001; Ilya P. Razenshteyn (CoRR, 2018)

Chasing Nested Convex Bodies Nearly Optimally. - Sébastien Bubeck; Yin Tat Lee; Yuanzhi Li; Mark Sellke (CoRR, 2018)

Adversarial Examples from Cryptographic Pseudo-Random Generators. - Sébastien Bubeck; Yin Tat Lee; Eric Price 001; Ilya P. Razenshteyn (CoRR, 2018)

Sampling from a Log-Concave Distribution with Projected Langevin Monte Carlo. - Sébastien Bubeck; Ronen Eldan; Joseph Lehec (Discret. Comput. Geom., 2018)

Near-optimal method for highly smooth convex optimization. - Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; Yuanzhi Li; Aaron Sidford (COLT, 2019)

Improved Path-length Regret Bounds for Bandits. - Sébastien Bubeck; Yuanzhi Li; Haipeng Luo; Chen-Yu Wei (COLT, 2019)

Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. - Alexander V. Gasnikov; Pavel E. Dvurechensky; Eduard Gorbunov; Evgeniya A. Vorontsova; Daniil Selikhanovych; César A. Uribe; Bo Jiang 007; Haoyue Wang; Shuzhong Zhang; Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; et al. (COLT, 2019)

Adversarial examples from computational constraints. - Sébastien Bubeck; Yin Tat Lee; Eric Price 001; Ilya P. Razenshteyn (ICML, 2019)

Complexity of Highly Parallel Non-Smooth Convex Optimization. - Sébastien Bubeck; Qijia Jiang; Yin Tat Lee; Yuanzhi Li; Aaron Sidford (NeurIPS, 2019)

Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. - Hadi Salman; Jerry Li 001; Ilya P. Razenshteyn; Pengchuan Zhang; Huan Zhang; Sébastien Bubeck; Greg Yang (NeurIPS, 2019)

A Nearly-Linear Bound for Chasing Nested Convex Bodies. - C. J. Argue; Sébastien Bubeck; Michael B. Cohen; Anupam Gupta 001; Yin Tat Lee (SODA, 2019)

First-Order Regret Analysis of Thompson Sampling. - Sébastien Bubeck; Mark Sellke (CoRR, 2019)

Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing. - Sébastien Bubeck; Nikhil R. Devanur; Zhiyi Huang 002; Rad Niazadeh (J. Mach. Learn. Res., 2019)

Optimal Convergence Rates for Convex Distributed Optimization in Networks. - Kevin Scaman; Francis R. Bach; Sébastien Bubeck; Yin Tat Lee; Laurent Massoulié (J. Mach. Learn. Res., 2019)

The Entropic Barrier: Exponential Families, Log-Concave Geometry, and Self-Concordance. - Sébastien Bubeck; Ronen Eldan (Math. Oper. Res., 2019)

Competitively Chasing Convex Bodies. - Sébastien Bubeck; Yin Tat Lee; Yuanzhi Li; Mark Sellke (SIAM J. Comput., 2019)

Parametrized Metrical Task Systems. - Sébastien Bubeck; Yuval Rabani (APPROX-RANDOM, 2020)

Coordination without communication: optimal regret in two players multi-armed bandits. - Sébastien Bubeck; Thomas Budzinski (COLT, 2020)

Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without. - Sébastien Bubeck; Yuanzhi Li; Yuval Peres; Mark Sellke (COLT, 2020)

Entanglement is Necessary for Optimal Quantum Property Testing. - Sébastien Bubeck; Sitan Chen; Jerry Li 001 (FOCS, 2020)

Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization. - Hadrien Hendrikx; Lin Xiao; Sébastien Bubeck; Francis R. Bach; Laurent Massoulié (ICML, 2020)

Online Learning for Active Cache Synchronization. - Andrey Kolobov; Sébastien Bubeck; Julian Zimmert (ICML, 2020)

Network size and size of the weights in memorization with two-layers neural networks. - Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Dan Mikulincer (NeurIPS, 2020)

Chasing Nested Convex Bodies Nearly Optimally. - Sébastien Bubeck; Bo'az Klartag; Yin Tat Lee; Yuanzhi Li; Mark Sellke (SODA, 2020)

Network size and weights size for memorization with two-layers neural networks. - Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Dan Mikulincer (CoRR, 2020)

Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions. - Sébastien Bubeck; Thomas Budzinski; Mark Sellke (COLT, 2021)

A Law of Robustness for Two-Layers Neural Networks. - Sébastien Bubeck; Yuanzhi Li; Dheeraj M. Nagaraj (COLT, 2021)

Metrical Service Systems with Transformations. - Sébastien Bubeck; Niv Buchbinder; Christian Coester; Mark Sellke (ITCS, 2021)

Adversarial Examples in Multi-Layer Random ReLU Networks. - Peter L. Bartlett; Sébastien Bubeck; Yeshwanth Cherapanamjeri (NeurIPS, 2021)

A single gradient step finds adversarial examples on random two-layers neural networks. - Sébastien Bubeck; Yeshwanth Cherapanamjeri; Gauthier Gidel; Remi Tachet des Combes (NeurIPS, 2021)

Online Multiserver Convex Chasing and Optimization. - Sébastien Bubeck; Yuval Rabani; Mark Sellke (SODA, 2021)

FEAR: A Simple Lightweight Method to Rank Architectures. - Debadeepta Dey; Shital Shah; Sébastien Bubeck (CoRR, 2021)

Kernel-based Methods for Bandit Convex Optimization. - Sébastien Bubeck; Ronen Eldan; Yin Tat Lee (J. ACM, 2021)

Metrical Task Systems on Trees via Mirror Descent and Unfair Gluing. - Sébastien Bubeck; Michael B. Cohen; James R. Lee; Yin Tat Lee (SIAM J. Comput., 2021)

Shortest Paths without a Map, but with an Entropic Regularizer. - Sébastien Bubeck; Christian Coester; Yuval Rabani (FOCS, 2022)

Data Augmentation as Feature Manipulation. - Ruoqi Shen; Sébastien Bubeck; Suriya Gunasekar (ICML, 2022)

LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models. - Mojan Javaheripi; Gustavo de Rosa; Subhabrata Mukherjee; Shital Shah; Tomasz Religa; Caio César Teodoro Mendes; Sébastien Bubeck; Farinaz Koushanfar; Debadeepta Dey (NeurIPS, 2022)

Data Augmentation as Feature Manipulation: a story of desert cows and grass cows. - Ruoqi Shen; Sébastien Bubeck; Suriya Gunasekar (CoRR, 2022)

LiteTransformerSearch: Training-free On-device Search for Efficient Autoregressive Language Models. - Mojan Javaheripi; Shital Shah; Subhabrata Mukherjee; Tomasz L. Religa; Caio C. T. Mendes; Gustavo H. de Rosa; Sébastien Bubeck; Farinaz Koushanfar; Debadeepta Dey (CoRR, 2022)

Unveiling Transformers with LEGO: a synthetic reasoning task. - Yi Zhang; Arturs Backurs; Sébastien Bubeck; Ronen Eldan; Suriya Gunasekar; Tal Wagner (CoRR, 2022)

AutoMoE: Neural Architecture Search for Efficient Sparsely Activated Transformers. - Ganesh Jawahar; Subhabrata Mukherjee; Xiaodong Liu 003; Young Jin Kim 001; Muhammad Abdul-Mageed; Laks V. S. Lakshmanan; Ahmed Hassan Awadallah; Sébastien Bubeck; Jianfeng Gao 001 (CoRR, 2022)

AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation. - Ganesh Jawahar; Subhabrata Mukherjee; Xiaodong Liu 003; Young Jin Kim 001; Muhammad Abdul-Mageed; Laks V. S. Lakshmanan; Ahmed Hassan Awadallah; Sébastien Bubeck; Jianfeng Gao 001 (ACL (Findings), 2023)

On the complexity of finding stationary points of smooth functions in one dimension. - Sinho Chewi; Sébastien Bubeck; Adil Salim (ALT, 2023)

Learning threshold neurons via edge of stability. - Kwangjun Ahn; Sébastien Bubeck; Sinho Chewi; Yin Tat Lee; Felipe Suarez; Yi Zhang (NeurIPS, 2023)

The Randomized k-Server Conjecture Is False! - Sébastien Bubeck; Christian Coester; Yuval Rabani (STOC, 2023)

Sparks of Artificial General Intelligence: Early experiments with GPT-4. - Sébastien Bubeck; Varun Chandrasekaran; Ronen Eldan; Johannes Gehrke; Eric Horvitz; Ece Kamar; Peter Lee; Yin Tat Lee; Yuanzhi Li; Scott M. Lundberg; Harsha Nori; Hamid Palangi; et al. (CoRR, 2023)

Textbooks Are All You Need. - Suriya Gunasekar; Yi Zhang 001; Jyoti Aneja; Caio César Teodoro Mendes; Allie Del Giorno; Sivakanth Gopi; Mojan Javaheripi; Piero Kauffmann; Gustavo de Rosa; Olli Saarikivi; Adil Salim; Shital Shah; et al. (CoRR, 2023)

Textbooks Are All You Need II: phi-1.5 technical report. - Yuanzhi Li; Sébastien Bubeck; Ronen Eldan; Allie Del Giorno; Suriya Gunasekar; Yin Tat Lee (CoRR, 2023)

Positional Description Matters for Transformers Arithmetic. - Ruoqi Shen; Sébastien Bubeck; Ronen Eldan; Yin Tat Lee; Yuanzhi Li; Yi Zhang (CoRR, 2023)

TinyGSM: achieving >80% on GSM8k with small language models. - Bingbin Liu; Sébastien Bubeck; Ronen Eldan; Janardhan Kulkarni; Yuanzhi Li; Anh Nguyen; Rachel Ward; Yi Zhang (CoRR, 2023)

First-Order Bayesian Regret Analysis of Thompson Sampling. - Sébastien Bubeck; Mark Sellke (IEEE Trans. Inf. Theory, 2023)

A Universal Law of Robustness via Isoperimetry. - Sébastien Bubeck; Mark Sellke (J. ACM, 2023)

How to Fine-Tune Vision Models with SGD. - Ananya Kumar; Ruoqi Shen; Sébastien Bubeck; Suriya Gunasekar (ICLR, 2024)

Phi-3 Technical Report: A Highly Capable Language Model Locally on Your Phone. - Marah I Abdin; Sam Ade Jacobs; Ammar Ahmad Awan; Jyoti Aneja; Ahmed Awadallah 001; Hany Awadalla; Nguyen Bach; Amit Bahree; Arash Bakhtiari; Harkirat S. Behl; Alon Benhaim; Misha Bilenko; et al. (CoRR, 2024)

Small Language Models for Application Interactions: A Case Study. - Beibin Li; Yi Zhang; Sébastien Bubeck; Jeevan Pathuri; Ishai Menache (CoRR, 2024)

Phi-4 Technical Report. - Marah I Abdin; Jyoti Aneja; Harkirat S. Behl; Sébastien Bubeck; Ronen Eldan; Suriya Gunasekar; Michael Harrison; Russell J. Hewett; Mojan Javaheripi; Piero Kauffmann; James R. Lee; Yin Tat Lee; et al. (CoRR, 2024)

How to Trap a Gradient Flow. - Sébastien Bubeck; Dan Mikulincer (SIAM J. Comput., 2024)

gpt-oss-120b & gpt-oss-20b Model Card. - Sandhini Agarwal; Lama Ahmad; Jason Ai; Sam Altman; Andy Applebaum; Edwin Arbus; Rahul K. Arora; Yu Bai; Bowen Baker; Haiming Bao; Boaz Barak; Ally Bennett; et al. (CoRR, 2025)