Odalric-Ambrym Maillard

According to our database, Odalric-Ambrym Maillard authored at least 78 papers between 2009 and 2023.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2023

CRIMED: Lower and Upper Bounds on Regret for Bandits with Unbounded Stochastic Corruption.

Shubhada Agrawal, Timothée Mathieu, Debabrota Basu, Odalric-Ambrym Maillard

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

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

Fast Asymptotically Optimal Algorithms for Non-Parametric Stochastic Bandits.

Dorian Baudry, Fabien Pesquerel, Rémy Degenne, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Bregman Deviations of Generic Exponential Families.

Sayak Ray Chowdhury, Patrick Saux, Odalric Maillard, Aditya Gopalan

Proceedings of the Thirty Sixth Annual Conference on Learning Theory, 2023

Risk-aware linear bandits with convex loss.

Patrick Saux, Odalric Maillard

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Exploration in Reward Machines with Low Regret.

Hippolyte Bourel, Anders Jonsson, Odalric-Ambrym Maillard, Mohammad Sadegh Talebi

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration & Planning.

Reda Ouhamma, Debabrota Basu, Odalric Maillard

Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022

Collaborative Algorithms for Online Personalized Mean Estimation.

Mahsa Asadi, Aurélien Bellet, Odalric-Ambrym Maillard, Marc Tommasi

Trans. Mach. Learn. Res., 2022

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits.

Lilian Besson, Emilie Kaufmann, Odalric-Ambrym Maillard, Julien Seznec

J. Mach. Learn. Res., 2022

Bilinear Exponential Family of MDPs: Frequentist Regret Bound with Tractable Exploration and Planning.

Reda Ouhamma, Debabrota Basu, Odalric-Ambrym Maillard

CoRR, 2022

gym-DSSAT: a crop model turned into a Reinforcement Learning environment.

Romain Gautron, Emilio J. Padrón, Philippe Preux, Julien Bigot, Odalric-Ambrym Maillard, David Emukpere

CoRR, 2022

Bandits Corrupted by Nature: Lower Bounds on Regret and Robust Optimistic Algorithm.

Debabrota Basu, Odalric-Ambrym Maillard, Timothée Mathieu

CoRR, 2022

A channel selection game for multi-operator LoRaWAN deployments.

Kinda Khawam, Hassan Fawaz, Samer Lahoud, Odalric-Ambrym Maillard, Steven Martin

Comput. Networks, 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

IMED-RL: Regret optimal learning of ergodic Markov decision processes.

Fabien Pesquerel, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

From Optimality to Robustness: Dirichlet Sampling Strategies in Stochastic Bandits.

Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard

CoRR, 2021

Routine Bandits: Minimizing Regret on Recurring Problems.

Hassan Saber, Léo Saci, Odalric-Ambrym Maillard, Audrey Durand

Proceedings of the Machine Learning and Knowledge Discovery in Databases. Research Track, 2021

Indexed Minimum Empirical Divergence for Unimodal Bandits.

Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stochastic bandits with groups of similar arms.

Fabien Pesquerel, Hassan Saber, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge.

Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits.

Reda Ouhamma, Odalric-Ambrym Maillard, Vianney Perchet

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits.

Dorian Baudry, Patrick Saux, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Optimal Thompson Sampling strategies for support-aware CVaR bandits.

Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric Maillard

Proceedings of the 38th International Conference on Machine Learning, 2021

Learning Value Functions in Deep Policy Gradients using Residual Variance.

Yannis Flet-Berliac, Reda Ouhamma, Odalric-Ambrym Maillard, Philippe Preux

Proceedings of the 9th International Conference on Learning Representations, 2021

Improved Exploration in Factored Average-Reward MDPs.

Mohammad Sadegh Talebi, Anders Jonsson, Odalric Maillard

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

Reinforcement Learning in Parametric MDPs with Exponential Families.

Sayak Ray Chowdhury, Aditya Gopalan, Odalric-Ambrym Maillard

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Thompson Sampling for CVaR Bandits.

Dorian Baudry, Romain Gautron, Emilie Kaufmann, 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

Optimal Strategies for Graph-Structured Bandits.

Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard

CoRR, 2020

Forced-exploration free Strategies for Unimodal Bandits.

Hassan Saber, Pierre Ménard, Odalric-Ambrym Maillard

CoRR, 2020

Robust Estimation, Prediction and Control with Linear Dynamics and Generic Costs.

Edouard Leurent, Denis V. Efimov, Odalric-Ambrym Maillard

CoRR, 2020

Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs.

Edouard Leurent, Odalric-Ambrym Maillard, Denis V. Efimov

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Sub-sampling for Efficient Non-Parametric Bandit Exploration.

Dorian Baudry, Emilie Kaufmann, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Tightening Exploration in Upper Confidence Reinforcement Learning.

Hippolyte Bourel, Odalric Maillard, Mohammad Sadegh Talebi

Proceedings of the 37th International Conference on Machine Learning, 2020

Restarted Bayesian Online Change-point Detector achieves Optimal Detection Delay.

Réda Alami, Odalric Maillard, Raphaël Féraud

Proceedings of the 37th International Conference on Machine Learning, 2020

Robust-Adaptive Interval Predictive Control for Linear Uncertain Systems.

Edouard Leurent, Denis V. Efimov, Odalric-Ambrym Maillard

Proceedings of the 59th IEEE Conference on Decision and Control, 2020

Monte-Carlo Graph Search: the Value of Merging Similar States.

Edouard Leurent, Odalric-Ambrym Maillard

Proceedings of The 12th Asian Conference on Machine Learning, 2020

2019

Distribution-dependent and Time-uniform Bounds for Piecewise i.i.d Bandits.

Subhojyoti Mukherjee, 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

Approximate Robust Control of Uncertain Dynamical Systems.

Edouard Leurent, Yann Blanco, Denis V. Efimov, Odalric-Ambrym Maillard

CoRR, 2019

Practical Open-Loop Optimistic Planning.

Edouard Leurent, Odalric-Ambrym Maillard

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2019

Learning Multiple Markov Chains via Adaptive Allocation.

Mohammad Sadegh Talebi, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Regret Bounds for Learning State Representations in Reinforcement Learning.

Ronald Ortner, Matteo Pirotta, Alessandro Lazaric, Ronan Fruit, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Budgeted Reinforcement Learning in Continuous State Space.

Nicolas Carrara, Edouard Leurent, Romain Laroche, Tanguy Urvoy, Odalric-Ambrym Maillard, Olivier Pietquin

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

Sequential change-point detection: Laplace concentration of scan statistics and non-asymptotic delay bounds.

Odalric-Ambrym Maillard

Proceedings of the Algorithmic Learning Theory, 2019

Model-Based Reinforcement Learning Exploiting State-Action Equivalence.

Mahsa Asadi, Mohammad Sadegh Talebi, Hippolyte Bourel, Odalric-Ambrym Maillard

Proceedings of The 11th Asian Conference on Machine Learning, 2019

Mathematics of Statistical Sequential Decision Making. (Mathématique de la prise de décision séquentielle statistique).

Odalric-Ambrym Maillard

, 2019

2018

Streaming kernel regression with provably adaptive mean, variance, and regularization.

Audrey Durand, Odalric-Ambrym Maillard, Joelle Pineau

J. Mach. Learn. Res., 2018

Variance-Aware Regret Bounds for Undiscounted Reinforcement Learning in MDPs.

Mohammad Sadegh Talebi, Odalric-Ambrym Maillard

Proceedings of the Algorithmic Learning Theory, 2018

2017

The non-stationary stochastic multi-armed bandit problem.

Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard

Int. J. Data Sci. Anal., 2017

Spectral Learning from a Single Trajectory under Finite-State Policies.

Borja Balle, Odalric-Ambrym Maillard

Proceedings of the 34th International Conference on Machine Learning, 2017

Efficient tracking of a growing number of experts.

Jaouad Mourtada, Odalric-Ambrym Maillard

Proceedings of the International Conference on Algorithmic Learning Theory, 2017

Boundary Crossing for General Exponential Families.

Odalric-Ambrym Maillard

Proceedings of the International Conference on Algorithmic Learning Theory, 2017

2016

Low-rank Bandits with Latent Mixtures.

Aditya Gopalan, Odalric-Ambrym Maillard, Mohammadi Zaki

CoRR, 2016

Random Shuffling and Resets for the Non-stationary Stochastic Bandit Problem.

Robin Allesiardo, Raphaël Féraud, Odalric-Ambrym Maillard

CoRR, 2016

Pliable Rejection Sampling.

Akram Erraqabi, Michal Valko, Alexandra Carpentier, Odalric-Ambrym Maillard

Proceedings of the 33nd International Conference on Machine Learning, 2016

2014

Sub-sampling for Multi-armed Bandits.

Akram Baransi, Odalric-Ambrym Maillard, Shie Mannor

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2014

How hard is my MDP?" The distribution-norm to the rescue".

Odalric-Ambrym Maillard, Timothy A. Mann, Shie Mannor

Proceedings of the Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, 2014

Latent Bandits.

Odalric-Ambrym Maillard, Shie Mannor

Proceedings of the 31th International Conference on Machine Learning, 2014

Selecting Near-Optimal Approximate State Representations in Reinforcement Learning.

Ronald Ortner, Odalric-Ambrym Maillard, Daniil Ryabko

Proceedings of the Algorithmic Learning Theory - 25th International Conference, 2014

2013

Optimal Regret Bounds for Selecting the State Representation in Reinforcement Learning.

Odalric-Ambrym Maillard, Phuong Nguyen, Ronald Ortner, Daniil Ryabko

Proceedings of the 30th International Conference on Machine Learning, 2013

Robust Risk-Averse Stochastic Multi-armed Bandits.

Odalric-Ambrym Maillard

Proceedings of the Algorithmic Learning Theory - 24th International Conference, 2013

Competing with an Infinite Set of Models in Reinforcement Learning.

Phuong Nguyen, Odalric-Ambrym Maillard, Daniil Ryabko, Ronald Ortner

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Linear regression with random projections.

Odalric-Ambrym Maillard, Rémi Munos

J. Mach. Learn. Res., 2012

Hierarchical Optimistic Region Selection driven by Curiosity.

Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

Online allocation and homogeneous partitioning for piecewise constant mean-approximation.

Alexandra Carpentier, Odalric-Ambrym Maillard

Proceedings of the Advances in Neural Information Processing Systems 25: 26th Annual Conference on Neural Information Processing Systems 2012. Proceedings of a meeting held December 3-6, 2012

2011

(APPRENTISSAGE SÉQUENTIEL : Bandits, Statistique et Renforcement.

Odalric-Ambrym Maillard

PhD thesis, 2011

A Finite-Time Analysis of Multi-armed Bandits Problems with Kullback-Leibler Divergences.

Odalric-Ambrym Maillard, Rémi Munos, Gilles Stoltz

Proceedings of the COLT 2011, 2011

Adaptive Bandits: Towards the best history-dependent strategy.

Odalric-Ambrym Maillard, Rémi Munos

Proceedings of the Fourteenth International Conference on Artificial Intelligence and Statistics, 2011

Selecting the State-Representation in Reinforcement Learning.

Odalric-Ambrym Maillard, Rémi Munos, Daniil Ryabko

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

Sparse Recovery with Brownian Sensing.

Alexandra Carpentier, Odalric-Ambrym Maillard, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 24: 25th Annual Conference on Neural Information Processing Systems 2011. Proceedings of a meeting held 12-14 December 2011, 2011

2010

Finite-sample Analysis of Bellman Residual Minimization.

Odalric-Ambrym Maillard, Rémi Munos, Alessandro Lazaric, Mohammad Ghavamzadeh

Proceedings of the 2nd Asian Conference on Machine Learning, 2010

Online Learning in Adversarial Lipschitz Environments.

Odalric-Ambrym Maillard, Rémi Munos

Proceedings of the Machine Learning and Knowledge Discovery in Databases, 2010

Scrambled Objects for Least-Squares Regression.

Odalric-Ambrym Maillard, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

LSTD with Random Projections.

Mohammad Ghavamzadeh, Alessandro Lazaric, Odalric-Ambrym Maillard, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 23: 24th Annual Conference on Neural Information Processing Systems 2010. Proceedings of a meeting held 6-9 December 2010, 2010

2009

Compressed Least-Squares Regression.

Odalric-Ambrym Maillard, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 22: 23rd Annual Conference on Neural Information Processing Systems 2009. Proceedings of a meeting held 7-10 December 2009, 2009

Complexity versus Agreement for Many Views.

Odalric-Ambrym Maillard, Nicolas Vayatis

Proceedings of the Algorithmic Learning Theory, 20th International Conference, 2009