People


Emilie Kaufmann

Institution office homepage group
 Inria None None Former members of the LINCS

Books And Theses

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
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

Articles

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 M. Koolen, Aurélien 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 A. 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 M. Koolen,
NIPS 2017, Long Beach, United States
Maximin Action Identification - A New Bandit Framework for Games,
Aurélien Garivier, Emilie Kaufmann, Wouter M. Koolen,
COLT 2016, New-York, United States
On Explore-Then-Commit strategies,
Aurélien Garivier, Tor Lattimore, Emilie Kaufmann,
NIPS 2016, Barcelona, Spain
Optimal Best Arm Identification with Fixed Confidence,
Aurélien Garivier, Emilie Kaufmann,
COLT 2016, New York, United States
On the Complexity of A/B Testing,
Emilie Kaufmann , Olivier Cappé, Aurélien 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, Rémi Munos,
NIPS 2013
Thompson Sampling - An Asymptotically Optimal Finite-Time Analysis,
Emilie Kaufmann, Nathaniel Korda, Rémi Munos,
ALT 2012, Lyon, France
On Bayesian Upper Confidence Bounds for Bandit Problems,
Emilie Kaufmann, Olivier Cappé, Aurélien Garivier,
AISTATS 2012, La Palma, Iles Canaries, Spain

Journal articles

Asymptotically optimal algorithms for budgeted multiple play bandits,
Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz,
Machine Learning Journal 2019
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 Bayesian index policies for sequential resource allocation,
Emilie Kaufmann,
Annals of Statistics 2017
On the Complexity of Best-Arm Identification in Multi-Armed Bandit Models,
Emilie Kaufmann, Olivier Cappé, Aurélien Garivier,
Journal of Machine Learning Research 2016

Reports

Fixed-Confidence Guarantees for Bayesian Best-Arm Identification,
Xuedong Shang, Rianne de Heide, Emilie Kaufmann, Pierre Ménard, Michal Valko,
CoRR 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
Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals,
Emilie Kaufmann, Wouter M. Koolen,
CoRR 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,
Rémi Bonnefoi, Lilian Besson, Christophe Moy, Emilie Kaufmann, Jacques Palicot,
CoRR 2018
Learning the distribution with largest mean - two bandit frameworks,
Emilie Kaufmann, Aurélien 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, Rémi Munos,
CoRR 2012

Misc

A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players,
Etienne Boursier, Emilie Kaufmann, Abbas Mehrabian, Vianney Perchet,
2019
Non-Asymptotic Sequential Tests for Overlapping Hypotheses and application to near optimal arm identification in bandit models,
Aurélien Garivier, Emilie Kaufmann,
2019
The Generalized Likelihood Ratio Test meets klUCB: an Improved Algorithm for Piece-Wise Non-Stationary Bandits Le test du ratio de vraisemblance généralisé (GLRT) rencontre klUCB : un meilleur algorithme pour les bandits stationnaires par morceaux,
Lilian Besson, Emilie Kaufmann,
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