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Emilie Kaufmann

Emilie Kaufmann
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0  InriaNoneNoneFormer members of the LINCS

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

s

Active Coverage for PAC Reinforcement Learning,
Aymen Al Marjani, Andrea Tirinzoni, Emilie Kaufmann,
COLT 2023, Bangalore, India
Optimistic PAC Reinforcement Learning: the Instance-Dependent View,
Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann,
ALT 2023, Milan, Italy
Dealing with Unknown Variances in Best-Arm Identification,
Marc Jourdan, Remy Degenne, Emilie Kaufmann,
ALT 2023
Top Two Algorithms Revisited,
Marc Jourdan, Remy Degenne, Dorian Baudry, Rianne De Heide, Emilie Kaufmann,
NeurIPS 2022, New Orleans, United States
Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs,
Andrea Tirinzoni, Aymen Al Marjani, Emilie Kaufmann,
NeurIPS 2022, New Orleans, United States
Near-Optimal Collaborative Learning in Bandits,
Clemence Reda, Sattar Vakili, Emilie Kaufmann,
NeurIPS 2022, New Orleans, United States
Efficient Algorithms for Extreme Bandits,
Dorian Baudry, Yoan Russac, Emilie Kaufmann,
AISTATS 2022, Virtual Conference, Spain
Optimal Thompson Sampling strategies for support-aware CVaR bandits,
Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric Maillard,
ICML 2021, Virtual, United States
Fast active learning for pure exploration in reinforcement learning,
Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Emilie Kaufmann, Edouard Leurent, Michal Valko,
ICML 2021, Vienna, Austria
A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces,
Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko,
AISTATS 2021, San Diego / Virtual, United States
Episodic Reinforcement Learning in Finite MDPs: Minimax Lower Bounds Revisited,
Omar Darwiche Domingues, Pierre Menard, Emilie Kaufmann, Michal Valko,
ALT 2021, Paris / Virtual, France
Kernel-Based Reinforcement Learning: A Finite-Time Analysis,
Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko,
ICML 2021, Vienna / Virtual, Austria
Top-m identification for linear bandits,
Clemence Reda, Emilie Kaufmann, Andree Delahaye Duriez,
AISTATS 2021, Virtual, United States
Adaptive Reward-Free Exploration,
Emilie Kaufmann, Pierre Menard, Omar Darwiche Domingues, Anders Jonsson, Edouard Leurent, Michal Valko,
ALT 2021, Paris, France
Sub-sampling for Efficient Non-Parametric Bandit Exploration,
Dorian Baudry, Emilie Kaufmann, Odalric Ambrym Maillard,
NeurIPS 2020, Vancouver, Canada
Planning in Markov Decision Processes with Gap-Dependent Sample Complexity,
Anders Jonsson, Emilie Kaufmann, Pierre Menard, Omar Darwiche Domingues, Edouard Leurent, Michal Valko,
NeurIPS 2020, Vancouver, France
A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players,
Abbas Mehrabian, Etienne Boursier, Emilie Kaufmann, Vianney Perchet,
AISTATS 2020, Palermo, Italy
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling,
Cindy Trinh, Emilie Kaufmann, Claire Vernade, Richard Combes,
ALT 2020, San Diego, United States
Fixed-confidence guarantees for Bayesian best-arm identification,
Xuedong Shang, Rianne De Heide, Pierre Menard, Emilie Kaufmann, Michal Valko,
AISTATS 2020, Palermo, Italy
Analyse non asymptotique d'un test séquentiel de détection de rupture et application aux bandits non stationnaires Non-asymptotic analysis of a sequential rupture detection test and its application to non-stationary bandits,
Lilian Besson, Emilie Kaufmann,
GRETSI 2019 - XXVIIème Colloque francophone de traitement du signal et des images 2019, Lille, France
A simple dynamic bandit algorithm for hyper-parameter tuning,
Xuedong Shang, Emilie Kaufmann, Michal Valko,
Workshop on Automated Machine Learning at International Conference on Machine Learning 2019, Long Beach, United States
General parallel optimization without a metric,
Xuedong Shang, Emilie Kaufmann, Michal Valko,
Algorithmic Learning Theory 2019, Chicago, United States
General parallel optimization a without metric,
Xuedong Shang, Emilie Kaufmann, Michal Valko,
ALT 2019
Adaptive black-box optimization got easier: HCT only needs local smoothness,
Xuedong Shang, Emilie Kaufmann, Michal Valko,
European Workshop on Reinforcement Learning 2018, Lille, France
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling,
Emilie Kaufmann, Wouter Koolen, Aurelien Garivier,
NeurIPS 2018, Montréal, Canada
Corrupt Bandits for Preserving Local Privacy,
Pratik Gajane, Tanguy Urvoy, Emilie Kaufmann,
ALT 2018, Lanzarote, Spain
Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence,
Maryam Aziz, Jesse Anderton, Emilie Kaufmann, Javed Aslam,
ALT 2018, Lanzarote, Spain
Multi-Player Bandits Revisited Modèles de Bandits Multi-Joueurs Revisités,
Lilian Besson, Emilie Kaufmann,
Algorithmic Learning Theory 2018, Lanzarote, Spain
Aggregation of Multi-Armed Bandits Learning Algorithms for Opportunistic Spectrum Access Agrégation d'algorithmes d'apprentissage pour les bandits multi-bras appliquée à l'accès opportuniste au spectre,
Lilian Besson, Emilie Kaufmann, Christophe Moy,
IEEE WCNC - IEEE Wireless Communications and Networking Conference 2018, Barcelona, Spain
{Multi-Player Bandits Revisited},
Lilian Besson, Emilie Kaufmann,
ALT 2018
Aggregation of multi-armed bandits learning algorithms for opportunistic spectrum access,
Lilian Besson, Emilie Kaufmann, Christophe Moy,
WCNC 2018
Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings Apprentissage de Bandit Multi-Bras dans les réseaux Internet des Objets: l'apprentissage est utile même dans des cas non-stationnaires,
Rémi Bonnefoi, Lilian Besson, Christophe Moy, Emilie Kaufmann, Jacques Palicot,
CROWNCOM 2017 - 12th EAI International Conference on Cognitive Radio Oriented Wireless Networks 2017, Lisbon, Portugal
Monte-Carlo Tree Search by Best Arm Identification,
Emilie Kaufmann, Wouter Koolen,
NIPS 2017, Long Beach, United States
Maximin Action Identification: A New Bandit Framework for Games,
Aurelien Garivier, Emilie Kaufmann, Wouter Koolen,
COLT 2016, New-York, United States
On Explore-Then-Commit strategies,
Aurelien Garivier, Tor Lattimore, Emilie Kaufmann,
NIPS 2016, Barcelona, Spain
Optimal Best Arm Identification with Fixed Confidence,
Aurelien Garivier, Emilie Kaufmann,
COLT 2016, New York, United States
On the Complexity of A/B Testing,
Emilie Kaufmann , Olivier Cappe, Aurelien Garivier,
COLT 2014, Barcelona, Spain
Thompson Sampling for one-dimensial exponential family bandits,
Nathaniel Korda, Emilie Kaufmann, Rémi Munos,
NIPS 2013 - Neural Information Processing Systems Conference 2013, Lake Tahoe, United States
Information Complexity in Bandit Subset Selection,
Emilie Kaufmann, Shivaram Kalyanakrishnan,
COLT 2013, Princeton, United States
Thompson sampling for one-dimensional exponential family bandits,
Nathaniel Korda, Emilie Kaufmann, Rémi Munos,
Advances in Neural Information Processing Systems 2013, United States
Thompson Sampling for 1-Dimensional Exponential Family Bandits,
Nathaniel Korda, Emilie Kaufmann, Remi Munos,
NIPS 2013
Thompson Sampling: An Asymptotically Optimal Finite-Time Analysis,
Emilie Kaufmann, Nathaniel Korda, Remi Munos,
ALT 2012, Lyon, France
On Bayesian Upper Confidence Bounds for Bandit Problems,
Emilie Kaufmann, Olivier Cappe, Aurelien Garivier,
AISTATS 2012, La Palma, Iles Canaries, Spain

Journal articles

Efficient Change-Point Detection for Tackling Piecewise-Stationary Bandits,
Lilian Besson, Emilie Kaufmann, Odalric Ambrym Maillard, Julien Seznec,
Journal of Machine Learning Research 2022
Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals,
Emilie Kaufmann, Wouter Koolen,
Journal of Machine Learning Research 2021
Non-Asymptotic Sequential Tests for Overlapping Hypotheses and application to near optimal arm identification in bandit models,
Aurélien Garivier, Emilie Kaufmann,
Sequential Analysis 2021
On Multi-Armed Bandit Designs for Dose-Finding Trials,
Maryam Aziz, Emilie Kaufmann, Marie Karelle Riviere,
Journal of Machine Learning Research 2021
Machine learning applications in drug development,
Clémence Réda, Emilie Kaufmann, Andrée Delahaye-Duriez,
Computational and Structural Biotechnology Journal 2020
Asymptotically optimal algorithms for budgeted multiple play bandits,
Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz,
Machine Learning 2019
On Bayesian index policies for sequential resource allocation,
Emilie Kaufmann,
Annals of Statistics 2018
A spectral algorithm with additive clustering for the recovery of overlapping communities in networks,
Emilie Kaufmann, Thomas Bonald , Marc Lelarge ,
Theoretical Computer Science 2018
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models,
Emilie Kaufmann, Olivier Cappe, Aurelien Garivier,
Journal of Machine Learning Research 2016

Contributions to the Optimal Solution of Several Bandit Problems Contributions à la résolution optimale de différents problèmes de bandit,
Emilie Kaufmann,
2020

Editorship

De l'échantillonnage adaptatif à la résolution de jeux,
Nathanaël Fijalkow, Emilie Kaufmann,
2022

Misc

An ε-Best-Arm Identification Algorithm for Fixed-Confidence and Beyond,
Marc Jourdan, Remy Degenne, Emilie Kaufmann,
CoRR 2023
Adaptive Algorithms for Relaxed Pareto Set Identification,
Cyrille Kone, Emilie Kaufmann, Laura Richert,
CoRR 2023
Regret Bounds for Kernel-Based Reinforcement Learning,
Omar Darwiche Domingues, Pierre Menard, Matteo Pirotta, Emilie Kaufmann, Michal Valko,
CoRR 2020
Thompson Sampling for CVaR Bandits,
Dorian Baudry, Romain Gautron, Emilie Kaufmann, Odalric Ambrym Maillard,
CoRR 2020
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
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits Ce que peuvent et ne peuvent pas faire les astuces de doublement pour les bandits multi-bras,
Lilian Besson, Emilie Kaufmann,
2018
What Doubling Tricks Can and Can't Do for Multi-Armed Bandits,
Lilian Besson, Emilie Kaufmann,
CoRR 2018
Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings,
Remi Bonnefoi, Lilian Besson, Christophe Moy, Emilie Kaufmann, Jacques Palicot,
CoRR 2018
Learning the distribution with largest mean: two bandit frameworks,
Emilie Kaufmann, Aurelien Garivier,
ESAIM: Proceedings and Surveys 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
Asymptotically Optimal Algorithms for Multiple Play Bandits with Partial Feedback,
Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz,
CoRR 2016
Thompson Sampling: An Optimal Finite Time Analysis,
Emilie Kaufmann, Nathaniel Korda, Remi Munos,
CoRR 2012