Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis. - Emilie Kaufmann; Nathaniel Korda; Rémi Munos (ALT, 2012)

Thompson Sampling: An Optimal Finite Time Analysis - Emilie Kaufmann; Nathaniel Korda; Rémi Munos (CoRR, 2012)

On Bayesian Upper Confidence Bounds for Bandit Problems. - Emilie Kaufmann; Olivier Cappé; Aurélien Garivier (AISTATS, 2012)

Information Complexity in Bandit Subset Selection. - Emilie Kaufmann; Shivaram Kalyanakrishnan (COLT, 2013)

Thompson Sampling for 1-Dimensional Exponential Family Bandits. - Nathaniel Korda; Emilie Kaufmann; Rémi Munos (NIPS, 2013)

On the Complexity of A/B Testing. - Emilie Kaufmann; Olivier Cappé; Aurélien Garivier (COLT, 2014)

Analysis of bayesian and frequentist strategies for sequential resource allocation. (Analyse de stratégies bayésiennes et fréquentistes pour l'allocation séquentielle de ressources). - Emilie Kaufmann (2014)

Optimal Best Arm Identification with Fixed Confidence. - Aurélien Garivier; Emilie Kaufmann (COLT, 2016)

Maximin Action Identification: A New Bandit Framework for Games. - Aurélien Garivier; Emilie Kaufmann; Wouter M. Koolen (COLT, 2016)

On Explore-Then-Commit strategies. - Aurélien Garivier; Tor Lattimore; Emilie Kaufmann (NIPS, 2016)

Asymptotically Optimal Algorithms for Multiple Play Bandits with Partial Feedback. - Alexander Luedtke; Emilie Kaufmann; Antoine Chambaz (CoRR, 2016)

On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models. - Emilie Kaufmann; Olivier Cappé; Aurélien Garivier (J. Mach. Learn. Res., 2016)

Multi-Armed Bandit Learning in IoT Networks: Learning Helps Even in Non-stationary Settings. - Rémi Bonnefoi; Lilian Besson; Christophe Moy; Emilie Kaufmann; Jacques Palicot (CrownCom, 2017)

Monte-Carlo Tree Search by Best Arm Identification. - Emilie Kaufmann; Wouter M. Koolen (NIPS, 2017)

Learning the distribution with largest mean: two bandit frameworks. - Emilie Kaufmann; Aurélien Garivier (CoRR, 2017)

Corrupt Bandits for Privacy Preserving Input. - Pratik Gajane; Tanguy Urvoy; Emilie Kaufmann (CoRR, 2017)

Multi-Player Bandits Models Revisited. - Lilian Besson; Emilie Kaufmann (CoRR, 2017)

Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence. - Maryam Aziz; Jesse Anderton; Emilie Kaufmann; Javed A. Aslam (ALT, 2018)

{Multi-Player Bandits Revisited}. - Lilian Besson; Emilie Kaufmann (ALT, 2018)

Corrupt Bandits for Preserving Local Privacy. - Pratik Gajane; Tanguy Urvoy; Emilie Kaufmann (ALT, 2018)

Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling. - Emilie Kaufmann; Wouter M. Koolen; Aurélien Garivier (NeurIPS, 2018)

Aggregation of multi-armed bandits learning algorithms for opportunistic spectrum access. - Lilian Besson; Emilie Kaufmann; Christophe Moy (WCNC, 2018)

What Doubling Tricks Can and Can't Do for Multi-Armed Bandits. - Lilian Besson; Emilie Kaufmann (CoRR, 2018)

A spectral algorithm with additive clustering for the recovery of overlapping communities in networks. - Emilie Kaufmann; Thomas Bonald; Marc Lelarge (Theor. Comput. Sci., 2018)

General parallel optimization a without metric. - Xuedong Shang; Emilie Kaufmann; Michal Valko (ALT, 2019)

New Algorithms for Multiplayer Bandits when Arm Means Vary Among Players. - Emilie Kaufmann; Abbas Mehrabian (CoRR, 2019)

The Generalized Likelihood Ratio Test meets klUCB: an Improved Algorithm for Piece-Wise Non-Stationary Bandits. - Lilian Besson; Emilie Kaufmann (CoRR, 2019)

On Multi-Armed Bandit Designs for Phase I Clinical Trials. - Maryam Aziz; Emilie Kaufmann; Marie-Karelle Riviere (CoRR, 2019)

Asymptotically optimal algorithms for budgeted multiple play bandits. - Alexander Luedtke; Emilie Kaufmann; Antoine Chambaz (Mach. Learn., 2019)

A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players. - Abbas Mehrabian; Etienne Boursier; Emilie Kaufmann; Vianney Perchet (AISTATS, 2020)

Fixed-confidence guarantees for Bayesian best-arm identification. - Xuedong Shang; Rianne de Heide; Pierre Ménard; Emilie Kaufmann; Michal Valko (AISTATS, 2020)

Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling. - Cindy Trinh; Emilie Kaufmann; Claire Vernade; Richard Combes (ALT, 2020)

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

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity. - Anders Jonsson 001; Emilie Kaufmann; Pierre Ménard; Omar Darwiche Domingues; Edouard Leurent; Michal Valko (NeurIPS, 2020)

Regret Bounds for Kernel-Based Reinforcement Learning. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (CoRR, 2020)

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

A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (AISTATS, 2021)

Top-m identification for linear bandits. - Clémence Réda; Emilie Kaufmann; Andrée Delahaye-Duriez (AISTATS, 2021)

Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited. - Omar Darwiche Domingues; Pierre Ménard; Emilie Kaufmann; Michal Valko (ALT, 2021)

Adaptive Reward-Free Exploration. - Emilie Kaufmann; Pierre Ménard; Omar Darwiche Domingues; Anders Jonsson 001; Edouard Leurent; Michal Valko (ALT, 2021)

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

Kernel-Based Reinforcement Learning: A Finite-Time Analysis. - Omar Darwiche Domingues; Pierre Ménard; Matteo Pirotta; Emilie Kaufmann; Michal Valko (ICML, 2021)

Fast active learning for pure exploration in reinforcement learning. - Pierre Ménard; Omar Darwiche Domingues; Anders Jonsson 001; Emilie Kaufmann; Edouard Leurent; Michal Valko (ICML, 2021)

On Multi-Armed Bandit Designs for Dose-Finding Trials. - Maryam Aziz; Emilie Kaufmann; Marie-Karelle Riviere (J. Mach. Learn. Res., 2021)

Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals. - Emilie Kaufmann; Wouter M. Koolen (J. Mach. Learn. Res., 2021)

Efficient Algorithms for Extreme Bandits. - Dorian Baudry; Yoan Russac; Emilie Kaufmann (AISTATS, 2022)

Top Two Algorithms Revisited. - Marc Jourdan; Rémy Degenne; Dorian Baudry; Rianne de Heide; Emilie Kaufmann (NeurIPS, 2022)

Near-Optimal Collaborative Learning in Bandits. - Clémence Réda; Sattar Vakili; Emilie Kaufmann (NeurIPS, 2022)

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs. - Andrea Tirinzoni; Aymen Al Marjani; Emilie Kaufmann (NeurIPS, 2022)

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

Dealing with Unknown Variances in Best-Arm Identification. - Marc Jourdan; Rémy Degenne; Emilie Kaufmann (ALT, 2023)

Optimistic PAC Reinforcement Learning: the Instance-Dependent View. - Andrea Tirinzoni; Aymen Al Marjani; Emilie Kaufmann (ALT, 2023)

Active Coverage for PAC Reinforcement Learning. - Aymen Al Marjani; Andrea Tirinzoni; Emilie Kaufmann (COLT, 2023)

An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond. - Marc Jourdan; Rémy Degenne; Emilie Kaufmann (NeurIPS, 2023)

Adaptive Algorithms for Relaxed Pareto Set Identification. - Cyrille Kone; Emilie Kaufmann; Laura Richert (NeurIPS, 2023)

Towards Instance-Optimality in Online PAC Reinforcement Learning. - Aymen Al Marjani; Andrea Tirinzoni; Emilie Kaufmann (CoRR, 2023)

Bandit Pareto Set Identification: the Fixed Budget Setting. - Cyrille Kone; Emilie Kaufmann; Laura Richert (AISTATS, 2024)

Optimal Multi-Fidelity Best-Arm Identification. - Riccardo Poiani; Rémy Degenne; Emilie Kaufmann; Alberto Maria Metelli; Marcello Restelli (NeurIPS, 2024)

Finding good policies in average-reward Markov Decision Processes without prior knowledge. - Adrienne Tuynman; Rémy Degenne; Emilie Kaufmann (NeurIPS, 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)

Pareto Set Identification With Posterior Sampling. - Cyrille Kone; Marc Jourdan; Emilie Kaufmann (AISTATS, 2025)

Bandit Pareto Set Identification in a Multi-Output Linear Model. - Cyrille Kone; Emilie Kaufmann; Laura Richert (AISTATS, 2025)

Best-Arm Identification in Unimodal Bandits. - Riccardo Poiani; Marc Jourdan; Emilie Kaufmann; Rémy Degenne (AISTATS, 2025)

Constrained Pareto Set Identification with Bandit Feedback. - Cyrille Kone; Emilie Kaufmann; Laura Richert (CoRR, 2025)