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ORCIDSébastien Bubeck
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2020 – today
- 2023
[j22]Sébastien Bubeck
, Mark Sellke:
A Universal Law of Robustness via Isoperimetry. J. ACM 70(2): 10:1-10:18 (2023)- [j21]Sébastien Bubeck, Mark Sellke:
First-Order Bayesian Regret Analysis of Thompson Sampling. IEEE Trans. Inf. Theory 69(3): 1795-1823 (2023) - [c66]Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu, Young Jin Kim, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Ahmed Hassan Awadallah, Sébastien Bubeck, Jianfeng Gao:
AutoMoE: Heterogeneous Mixture-of-Experts with Adaptive Computation for Efficient Neural Machine Translation. ACL (Findings) 2023: 9116-9132 - [c65]Sinho Chewi, Sébastien Bubeck, Adil Salim:
On the complexity of finding stationary points of smooth functions in one dimension. ALT 2023: 358-374 - [c64]Sébastien Bubeck, Christian Coester, Yuval Rabani:
The Randomized k-Server Conjecture Is False! STOC 2023: 581-594 - [i74]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, Marco Túlio Ribeiro, Yi Zhang:
Sparks of Artificial General Intelligence: Early experiments with GPT-4. CoRR abs/2303.12712 (2023) - [i73]Suriya Gunasekar, Yi Zhang, Jyoti Aneja, Caio César Teodoro Mendes, Allie Del Giorno, Sivakanth Gopi, Mojan Javaheripi, Piero Kauffmann, Gustavo de Rosa, Olli Saarikivi, Adil Salim, Shital Shah, Harkirat Singh Behl, Xin Wang, Sébastien Bubeck, Ronen Eldan, Adam Tauman Kalai, Yin Tat Lee, Yuanzhi Li:
Textbooks Are All You Need. CoRR abs/2306.11644 (2023) - [i72]Yuanzhi Li, Sébastien Bubeck, Ronen Eldan, Allie Del Giorno, Suriya Gunasekar, Yin Tat Lee:
Textbooks Are All You Need II: phi-1.5 technical report. CoRR abs/2309.05463 (2023) - [i71]Ruoqi Shen, Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Yuanzhi Li, Yi Zhang:
Positional Description Matters for Transformers Arithmetic. CoRR abs/2311.14737 (2023) - 2022
- [c63]Sébastien Bubeck, Christian Coester, Yuval Rabani:
Shortest Paths without a Map, but with an Entropic Regularizer. FOCS 2022: 1102-1113 - [c62]Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar:
Data Augmentation as Feature Manipulation. ICML 2022: 19773-19808 - [c61]Mojan Javaheripi, Gustavo de Rosa, Subhabrata Mukherjee, Shital Shah, Tomasz Religa, Caio Cesar Teodoro Mendes, Sébastien Bubeck, Farinaz Koushanfar, Debadeepta Dey:
LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models. NeurIPS 2022 - [i70]Sébastien Bubeck, Christian Coester, Yuval Rabani:
Shortest Paths without a Map, but with an Entropic Regularizer. CoRR abs/2202.04551 (2022) - [i69]Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar:
Data Augmentation as Feature Manipulation: a story of desert cows and grass cows. CoRR abs/2203.01572 (2022) - [i68]Mojan Javaheripi, Shital Shah, Subhabrata Mukherjee, Tomasz L. Religa, Caio C. T. Mendes, Gustavo H. de Rosa, Sébastien Bubeck, Farinaz Koushanfar, Debadeepta Dey:
LiteTransformerSearch: Training-free On-device Search for Efficient Autoregressive Language Models. CoRR abs/2203.02094 (2022) - [i67]Yi Zhang, Arturs Backurs, Sébastien Bubeck, Ronen Eldan, Suriya Gunasekar, Tal Wagner:
Unveiling Transformers with LEGO: a synthetic reasoning task. CoRR abs/2206.04301 (2022) - [i66]Ganesh Jawahar, Subhabrata Mukherjee, Xiaodong Liu, Young Jin Kim, Muhammad Abdul-Mageed, Laks V. S. Lakshmanan, Ahmed Hassan Awadallah, Sébastien Bubeck, Jianfeng Gao:
AutoMoE: Neural Architecture Search for Efficient Sparsely Activated Transformers. CoRR abs/2210.07535 (2022) - [i65]Sébastien Bubeck, Christian Coester, Yuval Rabani:
The Randomized k-Server Conjecture is False! CoRR abs/2211.05753 (2022) - [i64]Ananya Kumar, Ruoqi Shen, Sébastien Bubeck, Suriya Gunasekar:
How to Fine-Tune Vision Models with SGD. CoRR abs/2211.09359 (2022) - [i63]Kwangjun Ahn, Sébastien Bubeck, Sinho Chewi, Yin Tat Lee, Felipe Suarez, Yi Zhang:
Learning threshold neurons via the "edge of stability". CoRR abs/2212.07469 (2022) - 2021
- [j20]Sébastien Bubeck, Ronen Eldan, Yin Tat Lee:
Kernel-based Methods for Bandit Convex Optimization. J. ACM 68(4): 25:1-25:35 (2021) - [j19]Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee:
Metrical Task Systems on Trees via Mirror Descent and Unfair Gluing. SIAM J. Comput. 50(3): 909-923 (2021) - [c60]Sébastien Bubeck, Yuanzhi Li, Dheeraj M. Nagaraj:
A Law of Robustness for Two-Layers Neural Networks. COLT 2021: 804-820 - [c59]Sébastien Bubeck, Thomas Budzinski, Mark Sellke:
Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions. COLT 2021: 821-822 - [c58]Sébastien Bubeck, Niv Buchbinder, Christian Coester, Mark Sellke:
Metrical Service Systems with Transformations. ITCS 2021: 21:1-21:20 - [c57]Peter L. Bartlett, Sébastien Bubeck, Yeshwanth Cherapanamjeri:
Adversarial Examples in Multi-Layer Random ReLU Networks. NeurIPS 2021: 9241-9252 - [c56]Sébastien Bubeck, Yeshwanth Cherapanamjeri, Gauthier Gidel, Remi Tachet des Combes:
A single gradient step finds adversarial examples on random two-layers neural networks. NeurIPS 2021: 10081-10091 - [c55]Sébastien Bubeck, Mark Sellke:
A Universal Law of Robustness via Isoperimetry. NeurIPS 2021: 28811-28822 - [c54]Sébastien Bubeck, Yuval Rabani, Mark Sellke:
Online Multiserver Convex Chasing and Optimization. SODA 2021: 2093-2104 - [i62]Sébastien Bubeck, Yeshwanth Cherapanamjeri, Gauthier Gidel, Rémi Tachet des Combes:
A single gradient step finds adversarial examples on random two-layers neural networks. CoRR abs/2104.03863 (2021) - [i61]Sébastien Bubeck, Mark Sellke:
A Universal Law of Robustness via Isoperimetry. CoRR abs/2105.12806 (2021) - [i60]Debadeepta Dey, Shital Shah, Sébastien Bubeck:
FEAR: A Simple Lightweight Method to Rank Architectures. CoRR abs/2106.04010 (2021) - [i59]Peter L. Bartlett, Sébastien Bubeck, Yeshwanth Cherapanamjeri:
Adversarial Examples in Multi-Layer Random ReLU Networks. CoRR abs/2106.12611 (2021) - 2020
- [c53]Sébastien Bubeck, Mark Sellke:
First-Order Bayesian Regret Analysis of Thompson Sampling. ALT 2020: 196-233 - [c52]Sébastien Bubeck, Yuval Rabani:
Parametrized Metrical Task Systems. APPROX-RANDOM 2020: 54:1-54:14 - [c51]Sébastien Bubeck, Thomas Budzinski:
Coordination without communication: optimal regret in two players multi-armed bandits. COLT 2020: 916-939 - [c50]Sébastien Bubeck, Dan Mikulincer:
How to Trap a Gradient Flow. COLT 2020: 940-960 - [c49]Sébastien Bubeck, Yuanzhi Li, Yuval Peres, Mark Sellke:
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without. COLT 2020: 961-987 - [c48]Sébastien Bubeck, Sitan Chen, Jerry Li:
Entanglement is Necessary for Optimal Quantum Property Testing. FOCS 2020: 692-703 - [c47]Hadrien Hendrikx, Lin Xiao, Sébastien Bubeck, Francis R. Bach, Laurent Massoulié:
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization. ICML 2020: 4203-4227 - [c46]Andrey Kolobov, Sébastien Bubeck, Julian Zimmert:
Online Learning for Active Cache Synchronization. ICML 2020: 5371-5380 - [c45]Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer:
Network size and size of the weights in memorization with two-layers neural networks. NeurIPS 2020 - [c44]Sébastien Bubeck, Bo'az Klartag, Yin Tat Lee, Yuanzhi Li, Mark Sellke:
Chasing Nested Convex Bodies Nearly Optimally. SODA 2020: 1496-1508 - [i58]Sébastien Bubeck, Dan Mikulincer:
How to trap a gradient flow. CoRR abs/2001.02968 (2020) - [i57]Sébastien Bubeck, Thomas Budzinski:
Coordination without communication: optimal regret in two players multi-armed bandits. CoRR abs/2002.07596 (2020) - [i56]Hadrien Hendrikx, Lin Xiao, Sébastien Bubeck, Francis R. Bach, Laurent Massoulié:
Statistically Preconditioned Accelerated Gradient Method for Distributed Optimization. CoRR abs/2002.10726 (2020) - [i55]Andrey Kolobov, Sébastien Bubeck, Julian Zimmert:
Online Learning for Active Cache Synchronization. CoRR abs/2002.12014 (2020) - [i54]Sébastien Bubeck, Yuval Rabani, Mark Sellke:
Online Multiserver Convex Chasing and Optimization. CoRR abs/2004.07346 (2020) - [i53]Sébastien Bubeck, Sitan Chen, Jerry Li:
Entanglement is Necessary for Optimal Quantum Property Testing. CoRR abs/2004.07869 (2020) - [i52]Sébastien Bubeck, Ronen Eldan, Yin Tat Lee, Dan Mikulincer:
Network size and weights size for memorization with two-layers neural networks. CoRR abs/2006.02855 (2020) - [i51]Sébastien Bubeck, Niv Buchbinder, Christian Coester, Mark Sellke:
Metrical Service Systems with Transformations. CoRR abs/2009.08266 (2020) - [i50]Sébastien Bubeck, Yuanzhi Li, Dheeraj Nagaraj:
A law of robustness for two-layers neural networks. CoRR abs/2009.14444 (2020) - [i49]Sébastien Bubeck, Thomas Budzinski, Mark Sellke:
Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions. CoRR abs/2011.03896 (2020)
2010 – 2019
- 2019
- [j18]Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh:
Multi-scale Online Learning: Theory and Applications to Online Auctions and Pricing. J. Mach. Learn. Res. 20: 62:1-62:37 (2019) - [j17]Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié:
Optimal Convergence Rates for Convex Distributed Optimization in Networks. J. Mach. Learn. Res. 20: 159:1-159:31 (2019) - [j16]Sébastien Bubeck, Ronen Eldan:
The Entropic Barrier: Exponential Families, Log-Concave Geometry, and Self-Concordance. Math. Oper. Res. 44(1): 264-276 (2019) - [c43]Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Near-optimal method for highly smooth convex optimization. COLT 2019: 492-507 - [c42]Sébastien Bubeck, Yuanzhi Li, Haipeng Luo, Chen-Yu Wei:
Improved Path-length Regret Bounds for Bandits. COLT 2019: 508-528 - [c41]Alexander V. Gasnikov, Pavel E. Dvurechensky, Eduard Gorbunov, Evgeniya A. Vorontsova, Daniil Selikhanovych, César A. Uribe, Bo Jiang, Haoyue Wang, Shuzhong Zhang, Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Near Optimal Methods for Minimizing Convex Functions with Lipschitz $p$-th Derivatives. COLT 2019: 1392-1393 - [c40]Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn:
Adversarial examples from computational constraints. ICML 2019: 831-840 - [c39]Hadi Salman, Jerry Li, Ilya P. Razenshteyn, Pengchuan Zhang, Huan Zhang, Sébastien Bubeck, Greg Yang:
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. NeurIPS 2019: 11289-11300 - [c38]Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Complexity of Highly Parallel Non-Smooth Convex Optimization. NeurIPS 2019: 13900-13909 - [c37]Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee:
Metrical task systems on trees via mirror descent and unfair gluing. SODA 2019: 89-97 - [c36]C. J. Argue, Sébastien Bubeck, Michael B. Cohen, Anupam Gupta, Yin Tat Lee:
A Nearly-Linear Bound for Chasing Nested Convex Bodies. SODA 2019: 117-122 - [c35]Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke:
Competitively chasing convex bodies. STOC 2019: 861-868 - [i48]Sébastien Bubeck, Yuanzhi Li, Haipeng Luo, Chen-Yu Wei:
Improved Path-length Regret Bounds for Bandits. CoRR abs/1901.10604 (2019) - [i47]Sébastien Bubeck, Mark Sellke:
First-Order Regret Analysis of Thompson Sampling. CoRR abs/1902.00681 (2019) - [i46]Sébastien Bubeck, Yuval Rabani:
Parametrized Metrical Task Systems. CoRR abs/1904.03874 (2019) - [i45]Sébastien Bubeck, Yuanzhi Li, Yuval Peres, Mark Sellke:
Non-Stochastic Multi-Player Multi-Armed Bandits: Optimal Rate With Collision Information, Sublinear Without. CoRR abs/1904.12233 (2019) - [i44]Hadi Salman, Greg Yang, Jerry Li, Pengchuan Zhang, Huan Zhang, Ilya P. Razenshteyn, Sébastien Bubeck:
Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers. CoRR abs/1906.04584 (2019) - [i43]Sébastien Bubeck, Qijia Jiang, Yin Tat Lee, Yuanzhi Li, Aaron Sidford:
Complexity of Highly Parallel Non-Smooth Convex Optimization. CoRR abs/1906.10655 (2019) - 2018
- [j15]Sébastien Bubeck, Ronen Eldan, Joseph Lehec:
Sampling from a Log-Concave Distribution with Projected Langevin Monte Carlo. Discret. Comput. Geom. 59(4): 757-783 (2018) - [c34]Sébastien Bubeck, Michael B. Cohen, Yuanzhi Li:
Sparsity, variance and curvature in multi-armed bandits. ALT 2018: 111-127 - [c33]Sébastien Bubeck, Philippe Rigollet:
Conference on Learning Theory 2018: Preface. COLT 2018: 1 - [c32]Zeyuan Allen-Zhu, Sébastien Bubeck, Yuanzhi Li:
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits. ICML 2018: 186-194 - [c31]Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Laurent Massoulié, Yin Tat Lee:
Optimal Algorithms for Non-Smooth Distributed Optimization in Networks. NeurIPS 2018: 2745-2754 - [c30]Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan:
Is Q-Learning Provably Efficient? NeurIPS 2018: 4868-4878 - [c29]Sébastien Bubeck, Michael B. Cohen, Yin Tat Lee, James R. Lee, Aleksander Madry:
k-server via multiscale entropic regularization. STOC 2018: 3-16 - [c28]Sébastien Bubeck, Michael B. Cohen, Yin Tat Lee, Yuanzhi Li:
An homotopy method for lp regression provably beyond self-concordance and in input-sparsity time. STOC 2018: 1130-1137 - [e1]Sébastien Bubeck, Vianney Perchet, Philippe Rigollet:
Conference On Learning Theory, COLT 2018, Stockholm, Sweden, 6-9 July 2018. Proceedings of Machine Learning Research 75, PMLR 2018 [contents] - [i42]Zeyuan Allen-Zhu, Sébastien Bubeck, Yuanzhi Li:
Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits. CoRR abs/1802.03386 (2018) - [i41]Sébastien Bubeck, Eric Price, Ilya P. Razenshteyn:
Adversarial examples from computational constraints. CoRR abs/1805.10204 (2018) - [i40]C. J. Argue, Sébastien Bubeck, Michael B. Cohen, Anupam Gupta, Yin Tat Lee:
A Nearly-Linear Bound for Chasing Nested Convex Bodies. CoRR abs/1806.08865 (2018) - [i39]Chi Jin, Zeyuan Allen-Zhu, Sébastien Bubeck, Michael I. Jordan:
Is Q-learning Provably Efficient? CoRR abs/1807.03765 (2018) - [i38]Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee:
Metrical task systems on trees via mirror descent and unfair gluing. CoRR abs/1807.04404 (2018) - [i37]Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke:
Competitively Chasing Convex Bodies. CoRR abs/1811.00887 (2018) - [i36]Sébastien Bubeck, Yin Tat Lee, Yuanzhi Li, Mark Sellke:
Chasing Nested Convex Bodies Nearly Optimally. CoRR abs/1811.00999 (2018) - [i35]Sébastien Bubeck, Yin Tat Lee, Eric Price, Ilya P. Razenshteyn:
Adversarial Examples from Cryptographic Pseudo-Random Generators. CoRR abs/1811.06418 (2018) - 2017
- [j14]Sébastien Bubeck, Luc Devroye, Gábor Lugosi:
Finding Adam in random growing trees. Random Struct. Algorithms 50(2): 158-172 (2017) - [c27]Kevin Scaman, Francis R. Bach, Sébastien Bubeck, Yin Tat Lee, Laurent Massoulié:
Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks. ICML 2017: 3027-3036 - [c26]Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh:
Online Auctions and Multi-scale Online Learning. EC 2017: 497-514 - [c25]Sébastien Bubeck, Yin Tat Lee, Ronen Eldan:
Kernel-based methods for bandit convex optimization. STOC 2017: 72-85 - [c24]Omer Angel, Sébastien Bubeck, Yuval Peres, Fan Wei:
Local max-cut in smoothed polynomial time. STOC 2017: 429-437 - [i34]Sébastien Bubeck, Nikhil R. Devanur, Zhiyi Huang, Rad Niazadeh:
Online Auctions and Multi-scale Online Learning. CoRR abs/1705.09700 (2017) - [i33]Sébastien Bubeck, Michael B. Cohen, Yuanzhi Li:
Sparsity, variance and curvature in multi-armed bandits. CoRR abs/1711.01037 (2017) - [i32]Sébastien Bubeck, Michael B. Cohen, James R. Lee, Yin Tat Lee, Aleksander Madry:
k-server via multiscale entropic regularization. CoRR abs/1711.01085 (2017) - [i31]Sébastien Bubeck, Michael B. Cohen, Yin Tat Lee, Yuanzhi Li:
An homotopy method for 𝓵p regression provably beyond self-concordance and in input-sparsity time. CoRR abs/1711.01328 (2017) - 2016
- [j13]Sébastien Bubeck, Nati Linial:
On the Local Profiles of Trees. J. Graph Theory 81(2): 109-119 (2016) - [j12]Sébastien Bubeck, Jian Ding, Ronen Eldan, Miklós Z. Rácz:
Testing for high-dimensional geometry in random graphs. Random Struct. Algorithms 49(3): 503-532 (2016) - [c23]Sébastien Bubeck, Ronen Eldan:
Multi-scale exploration of convex functions and bandit convex optimization. COLT 2016: 583-589 - [c22]Sébastien Bubeck, Yin Tat Lee:
Black-box Optimization with a Politician. ICML 2016: 1624-1631 - [i30]Sébastien Bubeck, Yin Tat Lee:
Black-box optimization with a politician. CoRR abs/1602.04847 (2016) - [i29]Sébastien Bubeck, Ronen Eldan, Yin Tat Lee:
Kernel-based methods for bandit convex optimization. CoRR abs/1607.03084 (2016) - [i28]Miklós Z. Rácz, Sébastien Bubeck:
Basic models and questions in statistical network analysis. CoRR abs/1609.03511 (2016) - [i27]Omer Angel, Sébastien Bubeck, Yuval Peres, Fan Wei:
Local max-cut in smoothed polynomial time. CoRR abs/1610.04807 (2016) - 2015
- [j11]Sébastien Bubeck:
Convex Optimization: Algorithms and Complexity. Found. Trends Mach. Learn. 8(3-4): 231-357 (2015) - [j10]Louigi Addario-Berry, Shankar Bhamidi, Sébastien Bubeck, Luc Devroye, Gábor Lugosi, Roberto Imbuzeiro Oliveira:
Exceptional rotations of random graphs: a VC theory. J. Mach. Learn. Res. 16: 1893-1922 (2015) - [j9]Sébastien Bubeck, Elchanan Mossel, Miklós Z. Rácz:
On the Influence of the Seed Graph in the Preferential Attachment Model. IEEE Trans. Netw. Sci. Eng. 2(1): 30-39 (2015) - [c21]Sébastien Bubeck, Ofer Dekel, Tomer Koren, Yuval Peres:
Bandit Convex Optimization: \(\sqrt{T}\) Regret in One Dimension. COLT 2015: 266-278 - [c20]Sébastien Bubeck, Ronen Eldan:
The entropic barrier: a simple and optimal universal self-concordant barrier. COLT 2015: 279 - [c19]Sébastien Bubeck, Ronen Eldan, Joseph Lehec:
Finite-Time Analysis of Projected Langevin Monte Carlo. NIPS 2015: 1243-1251 - [i26]Sébastien Bubeck, Ofer Dekel, Tomer Koren, Yuval Peres:
Bandit Convex Optimization: sqrt{T} Regret in One Dimension. CoRR abs/1502.06398 (2015) - [i25]Sébastien Bubeck, Yin Tat Lee, Mohit Singh:
A geometric alternative to Nesterov's accelerated gradient descent. CoRR abs/1506.08187 (2015) - [i24]Sébastien Bubeck, Ronen Eldan, Joseph Lehec:
Sampling from a log-concave distribution with Projected Langevin Monte Carlo. CoRR abs/1507.02564 (2015) - [i23]Sébastien Bubeck, Ronen Eldan:
Multi-scale exploration of convex functions and bandit convex optimization. CoRR abs/1507.06580 (2015) - [i22]Sébastien Bubeck, Shirshendu Ganguly:
Entropic CLT and phase transition in high-dimensional Wishart matrices. CoRR abs/1509.03258 (2015) - 2014
- [j8]Jean-Yves Audibert, Sébastien Bubeck, Gábor Lugosi:
Regret in Online Combinatorial Optimization. Math. Oper. Res. 39(1): 31-45 (2014) - [c18]Sébastien Bubeck, Che-Yu Liu:
Prior-free and prior-dependent regret bounds for Thompson Sampling. CISS 2014: 1-9 - [c17]Kevin G. Jamieson, Matthew Malloy, Robert D. Nowak, Sébastien Bubeck:
lil' UCB : An Optimal Exploration Algorithm for Multi-Armed Bandits. COLT 2014: 423-439 - [c16]Che-Yu Liu, Sébastien Bubeck:
Most Correlated Arms Identification. COLT 2014: 623-637 - [i21]Sébastien Bubeck, Elchanan Mossel, Miklós Z. Rácz:
On the influence of the seed graph in the preferential attachment model. CoRR abs/1401.4849 (2014) - [i20]Che-Yu Liu, Sébastien Bubeck:
Most Correlated Arms Identification. CoRR abs/1404.5903 (2014) - [i19]Sébastien Bubeck:
Theory of Convex Optimization for Machine Learning. CoRR abs/1405.4980 (2014) - [i18]Sébastien Bubeck, Ronen Eldan, Elchanan Mossel, Miklós Z. Rácz:
From trees to seeds: on the inference of the seed from large trees in the uniform attachment model. CoRR abs/1409.7685 (2014) - [i17]Sébastien Bubeck, Luc Devroye, Gábor Lugosi:
Finding Adam in random growing trees. CoRR abs/1411.3317 (2014) - [i16]Sébastien Bubeck, Jian Ding, Ronen Eldan, Miklós Z. Rácz:
Testing for high-dimensional geometry in random graphs. CoRR abs/1411.5713 (2014) - [i15]