Odalric-Ambrym Maillard

Publication Activity (10 Years)

Years Active: 2009-2024
Publications (10 Years): 68

Top Venues

CoRR

NeurIPS

ALT

ACML

Lille University of Science and Technology, France (PHD)

Education History

Lille University of Science and Technology, France

PHD

Publications

Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
How to Shrink Confidence Sets for Many Equivalent Discrete Distributions? CoRR (2024)
Shubhada Agrawal, Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. ALT (2024)
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)
Tuan Dam, Odalric-Ambrym Maillard, Emilie Kaufmann
Power Mean Estimation in Stochastic Monte-Carlo Tree_Search. CoRR (2024)
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 (2023)
Dorian Baudry, Fabien Pesquerel, Rémy Degenne, Odalric-Ambrym Maillard
Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits. NeurIPS (2023)
Shubhada Agrawal, Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard
CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption. CoRR (2023)
Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
Exploration in Reward Machines with Low Regret. AISTATS (2023)
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 (2023)
Hassan Saber, Fabien Pesquerel, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
Logarithmic regret in communicating MDPs: Leveraging known dynamics with bandits. ACML (2023)
Kinda Khawam, Hassan Fawaz, Samer Lahoud, Odalric-Ambrym Maillard, Steven Martin
A channel selection game for multi-operator LoRaWAN deployments. Comput. Networks 216 (2022)
Debabrota Basu, Odalric-Ambrym Maillard, Timothée Mathieu
Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm. CoRR (2022)
Reda Ouhamma, Debabrota Basu, Odalric-Ambrym Maillard
Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning. CoRR (2022)
Patrick Saux, Odalric-Ambrym Maillard
Risk-aware linear bandits with convex loss. CoRR (2022)
Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi
Collaborative Algorithms for Online Personalized Mean Estimation. Trans. Mach. Learn. Res. 2022 (2022)
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 (2022)
Sayak Ray Chowdhury, Patrick Saux, Odalric-Ambrym Maillard, Aditya Gopalan
Bregman Deviations of Generic Exponential Families. CoRR (2022)
Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec
Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits. J. Mach. Learn. Res. 23 (2022)
Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi
Collaborative Algorithms for Online Personalized Mean Estimation. CoRR (2022)
Fabien Pesquerel, Odalric-Ambrym Maillard
IMED-RL: Regret optimal learning of ergodic Markov decision processes. NeurIPS (2022)
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 (2022)
Sayak Ray Chowdhury, Aditya Gopalan, Odalric-Ambrym Maillard
Reinforcement Learning in Parametric MDPs with Exponential Families. AISTATS (2021)
Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard, Philippe Preux
Learning Value Functions in Deep Policy Gradients using Residual Variance. ICLR (2021)
Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard
From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits. CoRR (2021)
Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet
Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge. NeurIPS (2021)
Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard
From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits. NeurIPS (2021)
Hassan Saber, Léo Saci, Odalric-Ambrym Maillard, Audrey Durand
Routine Bandits: Minimizing Regret on Recurring Problems. ECML/PKDD (1) (2021)
Fabien Pesquerel, Hassan Saber, Odalric-Ambrym Maillard
Stochastic bandits with groups of similar arms. NeurIPS (2021)
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
Indexed Minimum Empirical Divergence for Unimodal Bandits. NeurIPS (2021)
Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet
Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits. NeurIPS (2021)
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
Indexed Minimum Empirical Divergence for Unimodal Bandits. CoRR (2021)
Edouard Leurent, Denis V. Efimov, Odalric-Ambrym Maillard
Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems. CoRR (2020)
Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard
Sub-sampling for Efficient Non-Parametric Bandit Exploration. NeurIPS (2020)
Edouard Leurent, Denis V. Efimov, Odalric-Ambrym Maillard
Robust Estimation, Prediction and Control with Linear Dynamics and Generic Costs. CoRR (2020)
Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric-Ambrym Maillard
Thompson Sampling for CVaR Bandits. CoRR (2020)
Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard, Philippe Preux
Is Standard Deviation the New Standard? Revisiting the Critic in Deep Policy Gradients. CoRR (2020)
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
Forced-exploration free Strategies for Unimodal Bandits. CoRR (2020)
Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard
Sub-sampling for Efficient Non-Parametric Bandit Exploration. CoRR (2020)
Edouard Leurent, Denis V. Efimov, Odalric-Ambrym Maillard
Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems. CDC (2020)
Edouard Leurent, Odalric-Ambrym Maillard, Denis V. Efimov
Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs. NeurIPS (2020)
Edouard Leurent, Odalric-Ambrym Maillard
Monte-Carlo Graph Search: the Value of Merging Similar States. ACML (2020)
Mohammad Sadegh Talebi, Anders Jonsson, Odalric-Ambrym Maillard
Improved Exploration in Factored Average-Reward MDPs. CoRR (2020)
Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard
Optimal Strategies for Graph-Structured Bandits. CoRR (2020)
Hippolyte Bourel, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi
Tightening Exploration in Upper Confidence Reinforcement Learning. CoRR (2020)
Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin
Scaling up budgeted reinforcement learning. CoRR (2019)
Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard
Regret Bounds for Learning State Representations in Reinforcement Learning. NeurIPS (2019)
Subhojyoti Mukherjee, Odalric-Ambrym Maillard
Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits. CoRR (2019)
Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel, Odalric-Ambrym Maillard
Model-Based Reinforcement Learning Exploiting State-Action Equivalence. CoRR (2019)
Odalric-Ambrym Maillard
Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds. ALT (2019)
Edouard Leurent, Yann Blanco, Denis V. Efimov, Odalric-Ambrym Maillard
Approximate Robust Control of Uncertain Dynamical Systems. CoRR (2019)
Edouard Leurent, Odalric-Ambrym Maillard
Practical Open-Loop Optimistic Planning. CoRR (2019)
M. Sadegh Talebi, Odalric-Ambrym Maillard
Learning Multiple Markov Chains via Adaptive Allocation. CoRR (2019)
Edouard Leurent, Odalric-Ambrym Maillard
Practical Open-Loop Optimistic Planning. ECML/PKDD (3) (2019)
Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel, Odalric-Ambrym Maillard
Model-Based Reinforcement Learning Exploiting State-Action Equivalence. ACML (2019)
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Learning Multiple Markov Chains via Adaptive Allocation. NeurIPS (2019)
Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin
Budgeted Reinforcement Learning in Continuous State Space. NeurIPS (2019)
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs. CoRR (2018)
Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau
Streaming kernel regression with provably adaptive mean, variance, and regularization. J. Mach. Learn. Res. 19 (2018)
Mohammad Sadegh Talebi, Odalric-Ambrym Maillard
Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs. ALT (2018)
Borja Balle, Odalric-Ambrym Maillard
Spectral Learning from a Single Trajectory under Finite-State Policies. ICML (2017)
Jaouad Mourtada, Odalric-Ambrym Maillard
Efficient tracking of a growing number of experts. CoRR (2017)
Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau
Streaming kernel regression with provably adaptive mean, variance, and regularization. CoRR (2017)
Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard
The non-stationary stochastic multi-armed bandit problem. Int. J. Data Sci. Anal. 3 (4) (2017)
Jaouad Mourtada, Odalric-Ambrym Maillard
Efficient tracking of a growing number of experts. ALT (2017)
Odalric-Ambrym Maillard
Boundary Crossing for General Exponential Families. ALT (2017)
Aditya Gopalan, Odalric-Ambrym Maillard, Mohammadi Zaki
Low-rank Bandits with Latent Mixtures. CoRR (2016)
Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard
Pliable Rejection Sampling. ICML (2016)
Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard
Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem. CoRR (2016)
Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. ALT (2014)
Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko
Selecting Near-Optimal Approximate State Representations in Reinforcement Learning. CoRR (2014)
Odalric-Ambrym Maillard, Timothy A. Mann, Shie Mannor
How hard is my MDP?" The distribution-norm to the rescue". NIPS (2014)
Odalric-Ambrym Maillard, Shie Mannor
Latent Bandits. ICML (2014)
Akram Baransi, Odalric-Ambrym Maillard, Shie Mannor
Sub-sampling for Multi-armed Bandits. ECML/PKDD (1) (2014)
Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning CoRR (2013)
Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko
Selecting the State-Representation in Reinforcement Learning CoRR (2013)
Odalric-Ambrym Maillard
Robust Risk-Averse Stochastic Multi-armed Bandits. ALT (2013)
Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko, Ronald Ortner
Competing with an Infinite Set of Models in Reinforcement Learning. AISTATS (2013)
Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko
Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning. ICML (1) (2013)
Odalric-Ambrym Maillard, Rémi Munos
Linear regression with random projections. J. Mach. Learn. Res. 13 (2012)
Odalric-Ambrym Maillard
Hierarchical Optimistic Region Selection driven by Curiosity. NIPS (2012)
Alexandra Carpentier, Odalric-Ambrym Maillard
Online allocation and homogeneous partitioning for piecewise constant mean-approximation. NIPS (2012)
Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko
Selecting the State-Representation in Reinforcement Learning. NIPS (2011)
Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz
A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences. COLT (2011)
Alexandra Carpentier, Odalric-Ambrym Maillard, Rémi Munos
Sparse Recovery with Brownian Sensing. NIPS (2011)
Odalric-Ambrym Maillard, Rémi Munos
Adaptive Bandits: Towards the best history-dependent strategy. AISTATS (2011)
Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, Rémi Munos
LSTD with Random Projections. NIPS (2010)
Odalric-Ambrym Maillard, Rémi Munos
Scrambled Objects for Least-Squares Regression. NIPS (2010)
Odalric-Ambrym Maillard, Rémi Munos, Alessandro Lazaric, Mohammad Ghavamzadeh
Finite-sample Analysis of Bellman Residual Minimization. ACML (2010)
Odalric-Ambrym Maillard, Rémi Munos
Online Learning in Adversarial Lipschitz Environments. ECML/PKDD (2) (2010)
Odalric-Ambrym Maillard, Nicolas Vayatis
Complexity versus Agreement for Many Views. ALT (2009)
Odalric-Ambrym Maillard, Rémi Munos
Compressed Least-Squares Regression. NIPS (2009)

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Publication Activity (10 Years)

Top Topics

Top Venues

Odalric-Ambrym Maillard