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ORCIDOdalric-Ambrym Maillard
Odalric Maillard
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- affiliation: Technion, Haifa, Faculty of Electrical Engineering
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2020 – today
- 2024
[j8]Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard:
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms. Trans. Mach. Learn. Res. 2024 (2024)- [c54]Shubhada Agrawal, Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard:
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. ALT 2024: 74-124 - [i37]Tuan Dam, Odalric-Ambrym Maillard, Emilie Kaufmann:
Power Mean Estimation in Stochastic Monte-Carlo Tree_Search. CoRR abs/2406.02235 (2024) - [i36]Odalric-Ambrym Maillard, Mohammad Sadegh Talebi:
How to Shrink Confidence Sets for Many Equivalent Discrete Distributions? CoRR abs/2407.15662 (2024) - 2023
- [c53]Reda Ouhamma, Debabrota Basu, Odalric Maillard:
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration & Planning. AAAI 2023: 9336-9344 - [c52]Hassan Saber, Fabien Pesquerel, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi:
Logarithmic regret in communicating MDPs: Leveraging known dynamics with bandits. ACML 2023: 1167-1182 - [c51]Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi:
Exploration in Reward Machines with Low Regret. AISTATS 2023: 4114-4146 - [c50]Patrick Saux, Odalric Maillard:
Risk-aware linear bandits with convex loss. AISTATS 2023: 7723-7754 - [c49]Sayak Ray Chowdhury, Patrick Saux, Odalric Maillard, Aditya Gopalan:
Bregman Deviations of Generic Exponential Families. COLT 2023: 394-449 - [c48]Dorian Baudry, Fabien Pesquerel, Rémy Degenne, Odalric-Ambrym Maillard:
Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits. NeurIPS 2023 - [i35]Timothée Mathieu, Riccardo Della Vecchia, Alena Shilova, Matheus Centa de Medeiros, Hector Kohler, Odalric-Ambrym Maillard, Philippe Preux:
AdaStop: sequential testing for efficient and reliable comparisons of Deep RL Agents. CoRR abs/2306.10882 (2023) - [i34]Tuan Dam, Pascal Stenger, Lukas Schneider, Joni Pajarinen, Carlo D'Eramo, Odalric-Ambrym Maillard:
Monte-Carlo tree search with uncertainty propagation via optimal transport. CoRR abs/2309.10737 (2023) - [i33]Shubhada Agrawal, Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard:
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. CoRR abs/2309.16563 (2023) - 2022
- [j7]Romain Gautron
, Odalric-Ambrym Maillard, Philippe Preux, Marc Corbeels, Régis Sabbadin:
Reinforcement learning for crop management support: Review, prospects and challenges. Comput. Electron. Agric. 200: 107182 (2022) - [j6]Kinda Khawam, Hassan Fawaz, Samer Lahoud, Odalric-Ambrym Maillard, Steven Martin:
A channel selection game for multi-operator LoRaWAN deployments. Comput. Networks 216: 109185 (2022) - [j5]Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec:
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits. J. Mach. Learn. Res. 23: 77:1-77:40 (2022) - [j4]Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi:
Collaborative Algorithms for Online Personalized Mean Estimation. Trans. Mach. Learn. Res. 2022 (2022) - [c47]Fabien Pesquerel, Odalric-Ambrym Maillard:
IMED-RL: Regret optimal learning of ergodic Markov decision processes. NeurIPS 2022 - [i32]Sayak Ray Chowdhury, Patrick Saux, Odalric-Ambrym Maillard, Aditya Gopalan:
Bregman Deviations of Generic Exponential Families. CoRR abs/2201.07306 (2022) - [i31]Debabrota Basu, Odalric-Ambrym Maillard, Timothée Mathieu:
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm. CoRR abs/2203.03186 (2022) - [i30]Romain Gautron, Emilio J. Padrón, Philippe Preux, Julien Bigot, Odalric-Ambrym Maillard, David Emukpere:
gym-DSSAT: a crop model turned into a Reinforcement Learning environment. CoRR abs/2207.03270 (2022) - [i29]Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi:
Collaborative Algorithms for Online Personalized Mean Estimation. CoRR abs/2208.11530 (2022) - [i28]Patrick Saux, Odalric-Ambrym Maillard:
Risk-aware linear bandits with convex loss. CoRR abs/2209.07154 (2022) - [i27]Reda Ouhamma, Debabrota Basu, Odalric-Ambrym Maillard:
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning. CoRR abs/2210.02087 (2022) - 2021
- [c46]Sayak Ray Chowdhury, Aditya Gopalan, Odalric-Ambrym Maillard:
Reinforcement Learning in Parametric MDPs with Exponential Families. AISTATS 2021: 1855-1863 - [c45]Mohammad Sadegh Talebi, Anders Jonsson, Odalric Maillard:
Improved Exploration in Factored Average-Reward MDPs. AISTATS 2021: 3988-3996 - [c44]Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard, Philippe Preux:
Learning Value Functions in Deep Policy Gradients using Residual Variance. ICLR 2021 - [c43]Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric Maillard:
Optimal Thompson Sampling strategies for support-aware CVaR bandits. ICML 2021: 716-726 - [c42]Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard:
Indexed Minimum Empirical Divergence for Unimodal Bandits. NeurIPS 2021: 7346-7356 - [c41]Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard:
From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits. NeurIPS 2021: 14029-14041 - [c40]Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet:
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits. NeurIPS 2021: 18577-18589 - [c39]Fabien Pesquerel, Hassan Saber, Odalric-Ambrym Maillard:
Stochastic bandits with groups of similar arms. NeurIPS 2021: 19461-19472 - [c38]Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet:
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge. NeurIPS 2021: 24430-24441 - [c37]Hassan Saber, Léo Saci, Odalric-Ambrym Maillard, Audrey Durand:
Routine Bandits: Minimizing Regret on Recurring Problems. ECML/PKDD (1) 2021: 3-18 - [i26]Reda Ouhamma, Odalric Maillard, Vianney Perchet:
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge. CoRR abs/2111.01602 (2021) - [i25]Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard:
From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits. CoRR abs/2111.09724 (2021) - [i24]Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard:
Indexed Minimum Empirical Divergence for Unimodal Bandits. CoRR abs/2112.01452 (2021) - 2020
- [c36]Edouard Leurent, Odalric-Ambrym Maillard:
Monte-Carlo Graph Search: the Value of Merging Similar States. ACML 2020: 577-592 - [c35]Edouard Leurent, Denis V. Efimov, Odalric-Ambrym Maillard:
Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems. CDC 2020: 1429-1434 - [c34]Réda Alami, Odalric Maillard, Raphaël Féraud:
Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay. ICML 2020: 211-221 - [c33]Hippolyte Bourel, Odalric Maillard, Mohammad Sadegh Talebi:
Tightening Exploration in Upper Confidence Reinforcement Learning. ICML 2020: 1056-1066 - [c32]Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard:
Sub-sampling for Efficient Non-Parametric Bandit Exploration. NeurIPS 2020 - [c31]Edouard Leurent, Odalric-Ambrym Maillard, Denis V. Efimov:
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs. NeurIPS 2020 - [i23]Edouard Leurent, Denis V. Efimov, Odalric-Ambrym Maillard:
Robust Estimation, Prediction and Control with Linear Dynamics and Generic Costs. CoRR abs/2002.10816 (2020) - [i22]Hippolyte Bourel, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi:
Tightening Exploration in Upper Confidence Reinforcement Learning. CoRR abs/2004.09656 (2020) - [i21]Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard:
Forced-exploration free Strategies for Unimodal Bandits. CoRR abs/2006.16569 (2020) - [i20]Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard:
Optimal Strategies for Graph-Structured Bandits. CoRR abs/2007.03224 (2020) - [i19]Edouard Leurent, Denis V. Efimov, Odalric-Ambrym Maillard:
Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems. CoRR abs/2007.10401 (2020) - [i18]Mohammad Sadegh Talebi, Anders Jonsson, Odalric-Ambrym Maillard:
Improved Exploration in Factored Average-Reward MDPs. CoRR abs/2009.04575 (2020) - [i17]Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard, Philippe Preux:
Is Standard Deviation the New Standard? Revisiting the Critic in Deep Policy Gradients. CoRR abs/2010.04440 (2020) - [i16]Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard:
Sub-sampling for Efficient Non-Parametric Bandit Exploration. CoRR abs/2010.14323 (2020) - [i15]Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric-Ambrym Maillard:
Thompson Sampling for CVaR Bandits. CoRR abs/2012.05754 (2020)
2010 – 2019
- 2019
- [b2]Odalric-Ambrym Maillard:
Mathematics of Statistical Sequential Decision Making. (Mathématique de la prise de décision séquentielle statistique). Lille University of Science and Technology, France, 2019 - [c30]Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel, Odalric-Ambrym Maillard:
Model-Based Reinforcement Learning Exploiting State-Action Equivalence. ACML 2019: 204-219 - [c29]Odalric-Ambrym Maillard:
Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds. ALT 2019: 610-632 - [c28]Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin:
Budgeted Reinforcement Learning in Continuous State Space. NeurIPS 2019: 9295-9305 - [c27]Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard:
Regret Bounds for Learning State Representations in Reinforcement Learning. NeurIPS 2019: 12717-12727 - [c26]Mohammad Sadegh Talebi, Odalric-Ambrym Maillard:
Learning Multiple Markov Chains via Adaptive Allocation. NeurIPS 2019: 13322-13332 - [c25]Edouard Leurent, Odalric-Ambrym Maillard:
Practical Open-Loop Optimistic Planning. ECML/PKDD (3) 2019: 69-85 - [i14]Edouard Leurent, Yann Blanco, Denis V. Efimov, Odalric-Ambrym Maillard:
Approximate Robust Control of Uncertain Dynamical Systems. CoRR abs/1903.00220 (2019) - [i13]Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin:
Scaling up budgeted reinforcement learning. CoRR abs/1903.01004 (2019) - [i12]Edouard Leurent, Odalric-Ambrym Maillard:
Practical Open-Loop Optimistic Planning. CoRR abs/1904.04700 (2019) - [i11]M. Sadegh Talebi, Odalric-Ambrym Maillard:
Learning Multiple Markov Chains via Adaptive Allocation. CoRR abs/1905.11128 (2019) - [i10]Subhojyoti Mukherjee, Odalric-Ambrym Maillard:
Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits. CoRR abs/1905.13159 (2019) - [i9]Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel, Odalric-Ambrym Maillard:
Model-Based Reinforcement Learning Exploiting State-Action Equivalence. CoRR abs/1910.04077 (2019) - 2018
- [j3]Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau:
Streaming kernel regression with provably adaptive mean, variance, and regularization. J. Mach. Learn. Res. 19: 17:1-17:34 (2018) - [c24]Mohammad Sadegh Talebi, Odalric-Ambrym Maillard:
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs. ALT 2018: 770-805 - [i8]Mohammad Sadegh Talebi, Odalric-Ambrym Maillard:
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs. CoRR abs/1803.01626 (2018) - 2017
- [j2]Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard:
The non-stationary stochastic multi-armed bandit problem. Int. J. Data Sci. Anal. 3(4): 267-283 (2017) - [c23]Odalric-Ambrym Maillard:
Boundary Crossing for General Exponential Families. ALT 2017: 151-184 - [c22]Jaouad Mourtada, Odalric-Ambrym Maillard:
Efficient tracking of a growing number of experts. ALT 2017: 517-539 - [c21]Borja Balle, Odalric-Ambrym Maillard:
Spectral Learning from a Single Trajectory under Finite-State Policies. ICML 2017: 361-370 - [i7]Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau:
Streaming kernel regression with provably adaptive mean, variance, and regularization. CoRR abs/1708.00768 (2017) - [i6]Jaouad Mourtada, Odalric-Ambrym Maillard:
Efficient tracking of a growing number of experts. CoRR abs/1708.09811 (2017) - 2016
- [c20]Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard:
Pliable Rejection Sampling. ICML 2016: 2121-2129 - [i5]Aditya Gopalan, Odalric-Ambrym Maillard, Mohammadi Zaki:
Low-rank Bandits with Latent Mixtures. CoRR abs/1609.01508 (2016) - [i4]Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard:
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem. CoRR abs/1609.02139 (2016) - 2014
- [c19]Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko:
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. ALT 2014: 140-154 - [c18]Odalric-Ambrym Maillard, Shie Mannor:
Latent Bandits. ICML 2014: 136-144 - [c17]Odalric-Ambrym Maillard, Timothy A. Mann, Shie Mannor:
How hard is my MDP?" The distribution-norm to the rescue". NIPS 2014: 1835-1843 - [c16]Akram Baransi, Odalric-Ambrym Maillard, Shie Mannor:
Sub-sampling for Multi-armed Bandits. ECML/PKDD (1) 2014: 115-131 - [i3]Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko:
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. CoRR abs/1405.2652 (2014) - 2013
- [c15]Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko, Ronald Ortner:
Competing with an Infinite Set of Models in Reinforcement Learning. AISTATS 2013: 463-471 - [c14]Odalric-Ambrym Maillard:
Robust Risk-Averse Stochastic Multi-armed Bandits. ALT 2013: 218-233 - [c13]Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko:
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. ICML (1) 2013: 543-551 - [i2]Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko:
Selecting the State-Representation in Reinforcement Learning. CoRR abs/1302.2552 (2013) - [i1]Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko:
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. CoRR abs/1302.2553 (2013) - 2012
- [j1]Odalric-Ambrym Maillard, Rémi Munos:
Linear regression with random projections. J. Mach. Learn. Res. 13: 2735-2772 (2012) - [c12]Odalric-Ambrym Maillard:
Hierarchical Optimistic Region Selection driven by Curiosity. NIPS 2012: 1457-1465 - [c11]Alexandra Carpentier, Odalric-Ambrym Maillard:
Online allocation and homogeneous partitioning for piecewise constant mean-approximation. NIPS 2012: 1970-1978 - 2011
- [b1]Odalric-Ambrym Maillard:
(APPRENTISSAGE SÉQUENTIEL : Bandits, Statistique et Renforcement. Lille University of Science and Technology, France, 2011 - [c10]Alexandra Carpentier, Odalric-Ambrym Maillard, Rémi Munos:
Sparse Recovery with Brownian Sensing. NIPS 2011: 1782-1790 - [c9]Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko:
Selecting the State-Representation in Reinforcement Learning. NIPS 2011: 2627-2635 - [c8]Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz:
A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. COLT 2011: 497-514 - [c7]Odalric-Ambrym Maillard, Rémi Munos:
Adaptive Bandits: Towards the best history-dependent strategy. AISTATS 2011: 570-578 - 2010
- [c6]Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, Rémi Munos:
LSTD with Random Projections. NIPS 2010: 721-729 - [c5]Odalric-Ambrym Maillard, Rémi Munos:
Scrambled Objects for Least-Squares Regression. NIPS 2010: 1549-1557 - [c4]Odalric-Ambrym Maillard, Rémi Munos:
Online Learning in Adversarial Lipschitz Environments. ECML/PKDD (2) 2010: 305-320 - [c3]Odalric-Ambrym Maillard, Rémi Munos, Alessandro Lazaric, Mohammad Ghavamzadeh:
Finite-sample Analysis of Bellman Residual Minimization. ACML 2010: 299-314
2000 – 2009
- 2009
- [c2]Odalric-Ambrym Maillard, Nicolas Vayatis:
Complexity versus Agreement for Many Views. ALT 2009: 232-246 - [c1]Odalric-Ambrym Maillard, Rémi Munos:
Compressed Least-Squares Regression. NIPS 2009: 1213-1221
Coauthor Index
Mohammad Sadegh Talebi
aka: M. Sadegh Talebi
aka: M. Sadegh Talebi
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