Emilie Kaufmann

According to our database, Emilie Kaufmann authored at least 61 papers between 2012 and 2024.

Collaborative distances:

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

2024

Best-Arm Identification in Unimodal Bandits.

Riccardo Poiani, Marc Jourdan, Emilie Kaufmann, Rémy Degenne

CoRR, 2024

Optimal Multi-Fidelity Best-Arm Identification.

Riccardo Poiani, Rémy Degenne, Emilie Kaufmann, Alberto Maria Metelli, Marcello Restelli

CoRR, 2024

Power Mean Estimation in Stochastic Monte-Carlo Tree_Search.

Tuan Dam, Odalric-Ambrym Maillard, Emilie Kaufmann

CoRR, 2024

Finding good policies in average-reward Markov Decision Processes without prior knowledge.

Adrienne Tuynman, Rémy Degenne, Emilie Kaufmann

CoRR, 2024

Bandit Pareto Set Identification: the Fixed Budget Setting.

Cyrille Kone, Emilie Kaufmann, Laura Richert

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Towards Instance-Optimality in Online PAC Reinforcement Learning.

Aymen Al Marjani, Andrea Tirinzoni, Emilie Kaufmann

CoRR, 2023

Adaptive Algorithms for Relaxed Pareto Set Identification.

Cyrille Kone, Emilie Kaufmann, Laura Richert

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

An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond.

Marc Jourdan, Rémy Degenne, Emilie Kaufmann

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

Active Coverage for PAC Reinforcement Learning.

Aymen Al Marjani, Andrea Tirinzoni, Emilie Kaufmann

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

Optimistic PAC Reinforcement Learning: the Instance-Dependent View.

Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

Dealing with Unknown Variances in Best-Arm Identification.

Marc Jourdan, Rémy Degenne, Emilie Kaufmann

Proceedings of the International Conference on Algorithmic Learning Theory, 2023

2022

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

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

J. Mach. Learn. Res., 2022

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs.

Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann

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

Near-Optimal Collaborative Learning in Bandits.

Clémence Réda, Sattar Vakili, Emilie Kaufmann

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

Top Two Algorithms Revisited.

Marc Jourdan, Rémy Degenne, Dorian Baudry, Rianne de Heide, Emilie Kaufmann

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

Efficient Algorithms for Extreme Bandits.

Dorian Baudry, Yoan Russac, Emilie Kaufmann

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals.

Emilie Kaufmann, Wouter M. Koolen

J. Mach. Learn. Res., 2021

On Multi-Armed Bandit Designs for Dose-Finding Trials.

Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere

J. Mach. Learn. Res., 2021

Fast active learning for pure exploration in reinforcement learning.

Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko

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

Kernel-Based Reinforcement Learning: A Finite-Time Analysis.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

Proceedings of the 38th International Conference on Machine Learning, 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

Adaptive Reward-Free Exploration.

Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko

Proceedings of the Algorithmic Learning Theory, 2021

Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited.

Omar Darwiche Domingues, Pierre Ménard, Emilie Kaufmann, Michal Valko

Proceedings of the Algorithmic Learning Theory, 2021

Top-m identification for linear bandits.

Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez

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

A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

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

Regret Bounds for Kernel-Based Reinforcement Learning.

Omar Darwiche Domingues, Pierre Ménard, Matteo Pirotta, Emilie Kaufmann, Michal Valko

CoRR, 2020

Planning in Markov Decision Processes with Gap-Dependent Sample Complexity.

Anders Jonsson, Emilie Kaufmann, Pierre Ménard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko

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

Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling.

Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes

Proceedings of the Algorithmic Learning Theory, 2020

Fixed-confidence guarantees for Bayesian best-arm identification.

Xuedong Shang, Rianne de Heide, Pierre Ménard, Emilie Kaufmann, Michal Valko

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players.

Abbas Mehrabian, Etienne Boursier, Emilie Kaufmann, Vianney Perchet

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Asymptotically optimal algorithms for budgeted multiple play bandits.

Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz

Mach. Learn., 2019

On Multi-Armed Bandit Designs for Phase I Clinical Trials.

Maryam Aziz, Emilie Kaufmann, Marie-Karelle Riviere

CoRR, 2019

The Generalized Likelihood Ratio Test meets klUCB: an Improved Algorithm for Piece-Wise Non-Stationary Bandits.

Lilian Besson, Emilie Kaufmann

CoRR, 2019

New Algorithms for Multiplayer Bandits when Arm Means Vary Among Players.

Emilie Kaufmann, Abbas Mehrabian

CoRR, 2019

General parallel optimization a without metric.

Xuedong Shang, Emilie Kaufmann, Michal Valko

Proceedings of the Algorithmic Learning Theory, 2019

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

What Doubling Tricks Can and Can't Do for Multi-Armed Bandits.

Lilian Besson, Emilie Kaufmann

CoRR, 2018

Aggregation of multi-armed bandits learning algorithms for opportunistic spectrum access.

Lilian Besson, Emilie Kaufmann, Christophe Moy

Proceedings of the 2018 IEEE Wireless Communications and Networking Conference, 2018

Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling.

Emilie Kaufmann, Wouter M. Koolen, Aurélien Garivier

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

Corrupt Bandits for Preserving Local Privacy.

Pratik Gajane, Tanguy Urvoy, Emilie Kaufmann

Proceedings of the Algorithmic Learning Theory, 2018

{Multi-Player Bandits Revisited}.

Lilian Besson, Emilie Kaufmann

Proceedings of the Algorithmic Learning Theory, 2018

Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence.

Maryam Aziz, Jesse Anderton, Emilie Kaufmann, Javed A. Aslam

Proceedings of the Algorithmic Learning Theory, 2018

2017

Multi-Player Bandits Models Revisited.

Lilian Besson, Emilie Kaufmann

CoRR, 2017

Corrupt Bandits for Privacy Preserving Input.

Pratik Gajane, Tanguy Urvoy, Emilie Kaufmann

CoRR, 2017

Learning the distribution with largest mean: two bandit frameworks.

Emilie Kaufmann, Aurélien Garivier

CoRR, 2017

Monte-Carlo Tree Search by Best Arm Identification.

Emilie Kaufmann, Wouter M. Koolen

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

Multi-Armed Bandit Learning in IoT Networks: Learning Helps Even in Non-stationary Settings.

Rémi Bonnefoi, Lilian Besson, Christophe Moy, Emilie Kaufmann, Jacques Palicot

Proceedings of the Cognitive Radio Oriented Wireless Networks, 2017

2016

On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models.

Emilie Kaufmann, Olivier Cappé, Aurélien Garivier

J. Mach. Learn. Res., 2016

Asymptotically Optimal Algorithms for Multiple Play Bandits with Partial Feedback.

Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz

CoRR, 2016

On Explore-Then-Commit strategies.

Aurélien Garivier, Tor Lattimore, Emilie Kaufmann

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

Maximin Action Identification: A New Bandit Framework for Games.

Aurélien Garivier, Emilie Kaufmann, Wouter M. Koolen

Proceedings of the 29th Conference on Learning Theory, 2016

Optimal Best Arm Identification with Fixed Confidence.

Aurélien Garivier, Emilie Kaufmann

Proceedings of the 29th Conference on Learning Theory, 2016

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

PhD thesis, 2014

On the Complexity of A/B Testing.

Emilie Kaufmann, Olivier Cappé, Aurélien Garivier

Proceedings of The 27th Conference on Learning Theory, 2014

2013

Thompson Sampling for 1-Dimensional Exponential Family Bandits.

Nathaniel Korda, Emilie Kaufmann, Rémi Munos

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Information Complexity in Bandit Subset Selection.

Emilie Kaufmann, Shivaram Kalyanakrishnan

Proceedings of the COLT 2013, 2013

2012

On Bayesian Upper Confidence Bounds for Bandit Problems.

Emilie Kaufmann, Olivier Cappé, Aurélien Garivier

Proceedings of the Fifteenth International Conference on Artificial Intelligence and Statistics, 2012

Thompson Sampling: An Optimal Finite Time Analysis

Emilie Kaufmann, Nathaniel Korda, Rémi Munos

CoRR, 2012

Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis.

Emilie Kaufmann, Nathaniel Korda, Rémi Munos

Proceedings of the Algorithmic Learning Theory - 23rd International Conference, 2012