Complexity versus Agreement for Many Views. - Odalric-Ambrym Maillard; Nicolas Vayatis (ALT, 2009)

Compressed Least-Squares Regression. - Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2009)

LSTD with Random Projections. - Mohammad Ghavamzadeh; Alessandro Lazaric; Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2010)

Scrambled Objects for Least-Squares Regression. - Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2010)

Online Learning in Adversarial Lipschitz Environments. - Odalric-Ambrym Maillard; Rémi Munos (ECML/PKDD (2), 2010)

Finite-sample Analysis of Bellman Residual Minimization. - Odalric-Ambrym Maillard; Rémi Munos; Alessandro Lazaric; Mohammad Ghavamzadeh (ACML, 2010)

Sparse Recovery with Brownian Sensing. - Alexandra Carpentier; Odalric-Ambrym Maillard; Rémi Munos (NIPS, 2011)

Selecting the State-Representation in Reinforcement Learning. - Odalric-Ambrym Maillard; Rémi Munos; Daniil Ryabko (NIPS, 2011)

Adaptive Bandits: Towards the best history-dependent strategy. - Odalric-Ambrym Maillard; Rémi Munos (AISTATS, 2011)

A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. - Odalric-Ambrym Maillard; Rémi Munos; Gilles Stoltz (COLT, 2011)

(APPRENTISSAGE SÉQUENTIEL : Bandits, Statistique et Renforcement. - Odalric-Ambrym Maillard (2011)

Online allocation and homogeneous partitioning for piecewise constant mean-approximation. - Alexandra Carpentier; Odalric-Ambrym Maillard (NIPS, 2012)

Hierarchical Optimistic Region Selection driven by Curiosity. - Odalric-Ambrym Maillard (NIPS, 2012)

Linear regression with random projections. - Odalric-Ambrym Maillard; Rémi Munos (J. Mach. Learn. Res., 2012)

Competing with an Infinite Set of Models in Reinforcement Learning. - Phuong Nguyen; Odalric-Ambrym Maillard; Daniil Ryabko; Ronald Ortner (AISTATS, 2013)

Robust Risk-Averse Stochastic Multi-armed Bandits. - Odalric-Ambrym Maillard (ALT, 2013)

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. - Odalric-Ambrym Maillard; Phuong Nguyen; Ronald Ortner; Daniil Ryabko (ICML (1), 2013)

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. - Ronald Ortner; Odalric-Ambrym Maillard; Daniil Ryabko (ALT, 2014)

Latent Bandits. - Odalric-Ambrym Maillard; Shie Mannor (ICML, 2014)

How hard is my MDP?" The distribution-norm to the rescue". - Odalric-Ambrym Maillard; Timothy A. Mann; Shie Mannor (NIPS, 2014)

Sub-sampling for Multi-armed Bandits. - Akram Baransi; Odalric-Ambrym Maillard; Shie Mannor (ECML/PKDD (1), 2014)

Pliable Rejection Sampling. - Akram Erraqabi; Michal Valko; Alexandra Carpentier; Odalric-Ambrym Maillard (ICML, 2016)

Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem. - Robin Allesiardo; Raphaël Féraud; Odalric-Ambrym Maillard (CoRR, 2016)

Low-rank Bandits with Latent Mixtures. - Aditya Gopalan; Odalric-Ambrym Maillard; Mohammadi Zaki (CoRR, 2016)

Boundary Crossing for General Exponential Families. - Odalric-Ambrym Maillard (ALT, 2017)

Efficient tracking of a growing number of experts. - Jaouad Mourtada; Odalric-Ambrym Maillard (ALT, 2017)

Spectral Learning from a Single Trajectory under Finite-State Policies. - Borja Balle; Odalric-Ambrym Maillard (ICML, 2017)

The non-stationary stochastic multi-armed bandit problem. - Robin Allesiardo; Raphaël Féraud; Odalric-Ambrym Maillard (Int. J. Data Sci. Anal., 2017)

Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs. - Mohammad Sadegh Talebi; Odalric-Ambrym Maillard (ALT, 2018)

Streaming kernel regression with provably adaptive mean, variance, and regularization. - Audrey Durand; Odalric-Ambrym Maillard; Joelle Pineau (J. Mach. Learn. Res., 2018)

Mathematics of Statistical Sequential Decision Making. (Mathématique de la prise de décision séquentielle statistique). - Odalric-Ambrym Maillard (2019)

Model-Based Reinforcement Learning Exploiting State-Action Equivalence. - Mahsa Asadi; Mohammad Sadegh Talebi; Hippolyte Bourel; Odalric-Ambrym Maillard (ACML, 2019)

Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds. - Odalric-Ambrym Maillard (ALT, 2019)

Budgeted Reinforcement Learning in Continuous State Space. - Nicolas Carrara; Edouard Leurent; Romain Laroche; Tanguy Urvoy; Odalric-Ambrym Maillard; Olivier Pietquin (NeurIPS, 2019)

Regret Bounds for Learning State Representations in Reinforcement Learning. - Ronald Ortner; Matteo Pirotta; Alessandro Lazaric; Ronan Fruit; Odalric-Ambrym Maillard (NeurIPS, 2019)

Learning Multiple Markov Chains via Adaptive Allocation. - Mohammad Sadegh Talebi; Odalric-Ambrym Maillard (NeurIPS, 2019)

Practical Open-Loop Optimistic Planning. - Edouard Leurent; Odalric-Ambrym Maillard (ECML/PKDD (3), 2019)

Approximate Robust Control of Uncertain Dynamical Systems. - Edouard Leurent; Yann Blanco; Denis V. Efimov; Odalric-Ambrym Maillard (CoRR, 2019)

Scaling up budgeted reinforcement learning. - Nicolas Carrara; Edouard Leurent; Romain Laroche; Tanguy Urvoy; Odalric-Ambrym Maillard; Olivier Pietquin (CoRR, 2019)

Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits. - Subhojyoti Mukherjee; Odalric-Ambrym Maillard (CoRR, 2019)

Monte-Carlo Graph Search: the Value of Merging Similar States. - Edouard Leurent; Odalric-Ambrym Maillard (ACML, 2020)

Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems. - Edouard Leurent; Denis V. Efimov; Odalric-Ambrym Maillard (CDC, 2020)

Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay. - Réda Alami; Odalric Maillard; Raphaël Féraud (ICML, 2020)

Tightening Exploration in Upper Confidence Reinforcement Learning. - Hippolyte Bourel; Odalric Maillard; Mohammad Sadegh Talebi (ICML, 2020)

Sub-sampling for Efficient Non-Parametric Bandit Exploration. - Dorian Baudry; Emilie Kaufmann; Odalric-Ambrym Maillard (NeurIPS, 2020)

Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs. - Edouard Leurent; Odalric-Ambrym Maillard; Denis V. Efimov (NeurIPS, 2020)

Robust Estimation, Prediction and Control with Linear Dynamics and Generic Costs. - Edouard Leurent; Denis V. Efimov; Odalric-Ambrym Maillard (CoRR, 2020)

Forced-exploration free Strategies for Unimodal Bandits. - Hassan Saber; Pierre Ménard; Odalric-Ambrym Maillard (CoRR, 2020)

Optimal Strategies for Graph-Structured Bandits. - Hassan Saber; Pierre Ménard; Odalric-Ambrym Maillard (CoRR, 2020)

Is Standard Deviation the New Standard? Revisiting the Critic in Deep Policy Gradients. - Yannis Flet-Berliac; Reda Ouhamma; Odalric-Ambrym Maillard; Philippe Preux (CoRR, 2020)

Thompson Sampling for CVaR Bandits. - Dorian Baudry; Romain Gautron; Emilie Kaufmann; Odalric-Ambrym Maillard (CoRR, 2020)

Reinforcement Learning in Parametric MDPs with Exponential Families. - Sayak Ray Chowdhury; Aditya Gopalan; Odalric-Ambrym Maillard (AISTATS, 2021)

Improved Exploration in Factored Average-Reward MDPs. - Mohammad Sadegh Talebi; Anders Jonsson 001; Odalric Maillard (AISTATS, 2021)

Learning Value Functions in Deep Policy Gradients using Residual Variance. - Yannis Flet-Berliac; Reda Ouhamma; Odalric-Ambrym Maillard; Philippe Preux (ICLR, 2021)

Optimal Thompson Sampling strategies for support-aware CVaR bandits. - Dorian Baudry; Romain Gautron; Emilie Kaufmann; Odalric Maillard (ICML, 2021)

From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits. - Dorian Baudry; Patrick Saux; Odalric-Ambrym Maillard (NeurIPS, 2021)

Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits. - Reda Ouhamma; Odalric-Ambrym Maillard; Vianney Perchet (NeurIPS, 2021)

Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge. - Reda Ouhamma; Odalric-Ambrym Maillard; Vianney Perchet (NeurIPS, 2021)

Stochastic bandits with groups of similar arms. - Fabien Pesquerel; Hassan Saber; Odalric-Ambrym Maillard (NeurIPS, 2021)

Indexed Minimum Empirical Divergence for Unimodal Bandits. - Hassan Saber; Pierre Ménard; Odalric-Ambrym Maillard (NeurIPS, 2021)

Routine Bandits: Minimizing Regret on Recurring Problems. - Hassan Saber; Léo Saci; Odalric-Ambrym Maillard; Audrey Durand (ECML/PKDD (1), 2021)

From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits. - Dorian Baudry; Patrick Saux; Odalric-Ambrym Maillard (CoRR, 2021)

IMED-RL: Regret optimal learning of ergodic Markov decision processes. - Fabien Pesquerel; Odalric-Ambrym Maillard (NeurIPS, 2022)

Reinforcement learning for crop management support: Review, prospects and challenges. - Romain Gautron; Odalric-Ambrym Maillard; Philippe Preux; Marc Corbeels; Régis Sabbadin (Comput. Electron. Agric., 2022)

A channel selection game for multi-operator LoRaWAN deployments. - Kinda Khawam; Hassan Fawaz; Samer Lahoud; Odalric-Ambrym Maillard; Steven Martin 001 (Comput. Networks, 2022)

Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm. - Debabrota Basu; Odalric-Ambrym Maillard; Timothée Mathieu (CoRR, 2022)

gym-DSSAT: a crop model turned into a Reinforcement Learning environment. - Romain Gautron; Emilio J. Padrón 001; Philippe Preux; Julien Bigot; Odalric-Ambrym Maillard; David Emukpere (CoRR, 2022)

Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning. - Reda Ouhamma; Debabrota Basu; Odalric-Ambrym Maillard (CoRR, 2022)

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits. - Lilian Besson; Emilie Kaufmann; Odalric-Ambrym Maillard; Julien Seznec (J. Mach. Learn. Res., 2022)

Collaborative Algorithms for Online Personalized Mean Estimation. - Mahsa Asadi; Aurélien Bellet; Odalric-Ambrym Maillard; Marc Tommasi (Trans. Mach. Learn. Res., 2022)

Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration & Planning. - Reda Ouhamma; Debabrota Basu; Odalric Maillard (AAAI, 2023)

Logarithmic regret in communicating MDPs: Leveraging known dynamics with bandits. - Hassan Saber; Fabien Pesquerel; Odalric-Ambrym Maillard; Mohammad Sadegh Talebi (ACML, 2023)

Exploration in Reward Machines with Low Regret. - Hippolyte Bourel; Anders Jonsson 001; Odalric-Ambrym Maillard; Mohammad Sadegh Talebi (AISTATS, 2023)

Risk-aware linear bandits with convex loss. - Patrick Saux; Odalric Maillard (AISTATS, 2023)

Bregman Deviations of Generic Exponential Families. - Sayak Ray Chowdhury; Patrick Saux; Odalric Maillard; Aditya Gopalan (COLT, 2023)

Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits. - Dorian Baudry; Fabien Pesquerel; Rémy Degenne; Odalric-Ambrym Maillard (NeurIPS, 2023)

AdaStop: sequential testing for efficient and reliable comparisons of Deep RL Agents. - Timothée Mathieu; Riccardo Della Vecchia; Alena Shilova; Matheus Centa de Medeiros; Hector Kohler; Odalric-Ambrym Maillard; Philippe Preux (CoRR, 2023)

Monte-Carlo tree search with uncertainty propagation via optimal transport. - Tuan Dam; Pascal Stenger; Lukas Schneider; Joni Pajarinen; Carlo D'Eramo; Odalric-Ambrym Maillard (CoRR, 2023)

CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. - Shubhada Agrawal; Timothée Mathieu; Debabrota Basu; Odalric-Ambrym Maillard (ALT, 2024)

Évaluation de critères de sélection de noyaux pour la régression Ridge à noyau dans un contexte de petits jeux de données. - Frédérick Fabre Ferber; Dominique Gay; Jean-Christophe Soulié; Jean Diatta; Odalric-Ambrym Maillard (EGC, 2024)

Bandits with Multimodal Structure. - Hassan Saber; Odalric-Ambrym Maillard (RLJ, 2024)

Power Mean Estimation in Stochastic Monte-Carlo Tree Search. - Tuan Dam; Odalric-Ambrym Maillard; Emilie Kaufmann (UAI, 2024)

Power Mean Estimation in Stochastic Monte-Carlo Tree_Search. - Tuan Dam; Odalric-Ambrym Maillard; Emilie Kaufmann (CoRR, 2024)

How to Shrink Confidence Sets for Many Equivalent Discrete Distributions? - Odalric-Ambrym Maillard; Mohammad Sadegh Talebi (CoRR, 2024)

Hierarchical Subspaces of Policies for Continual Offline Reinforcement Learning. - Anthony Kobanda; Rémy Portelas; Odalric-Ambrym Maillard; Ludovic Denoyer (CoRR, 2024)

Provably Efficient Exploration in Reward Machines with Low Regret. - Hippolyte Bourel; Anders Jonsson 001; Odalric-Ambrym Maillard; Chenxiao Ma; Mohammad Sadegh Talebi (CoRR, 2024)

Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithms. - Timothée Mathieu; Debabrota Basu; Odalric-Ambrym Maillard (Trans. Mach. Learn. Res., 2024)

AdaStop: adaptive statistical testing for sound comparisons of Deep RL agents. - Timothée Mathieu; Matheus Centa; Riccardo Della Vecchia; Hector Kohler; Alena Shilova; Odalric-Ambrym Maillard; Philippe Preux (Trans. Mach. Learn. Res., 2024)

A Continual Offline Reinforcement Learning Benchmark for Navigation Tasks. - Anthony Kobanda; Odalric-Ambrym Maillard; Rémy Portelas (CoG, 2025)

Kriging and Gaussian Process Interpolation for Georeferenced Data Augmentation. - Frédérick Fabre Ferber; Dominique Gay; Jean-Christophe Soulié; Jean Diatta; Odalric-Ambrym Maillard (CoRR, 2025)

Interpolation pour l'augmentation de donnees : Application à la gestion des adventices de la canne a sucre a la Reunion. - Frédérick Fabre Ferber; Dominique Gay; Jean-Christophe Soulié; Jean Diatta; Odalric-Ambrym Maillard (CoRR, 2025)

The regret lower bound for communicating Markov Decision Processes. - Victor Boone; Odalric-Ambrym Maillard (CoRR, 2025)

Leveraging priors on distribution functions for multi-arm bandits. - Sumit Vashishtha; Odalric-Ambrym Maillard (CoRR, 2025)

Offline Goal-Conditioned Reinforcement Learning with Projective Quasimetric Planning. - Anthony Kobanda; Waris Radji; Mathieu Petitbois; Odalric-Ambrym Maillard; Rémy Portelas (CoRR, 2025)