Emilie Kaufmann

Institution ■ Inria Group 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

		⇣		Adaptive black-box optimization got easier: HCT only needs local smoothness, Xuedong Shang, Emilie Kaufmann, Michal Valko, EWRL 2018 - 14th European Workshop on Reinforcement Learning 2018, Lille, France
		⇣	⇣	Corrupt Bandits for Preserving Local Privacy, Pratik Gajane, Tanguy Urvoy, Emilie Kaufmann, ALT 2018, Lanzarote, Spain, 387-412
		⇣	⇣	Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence, Maryam Aziz, Jesse Anderton, Emilie Kaufmann, Javed A. Aslam, ALT 2018, Lanzarote, Spain, 3-24
		⇣		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, 56-92
			⇣	Aggregation of multi-armed bandits learning algorithms for opportunistic spectrum access, Lilian Besson, Emilie Kaufmann, Christophe Moy, WCNC 2018, 1-6
		⇣		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, 4904-4913
		⇣	⇣	Maximin Action Identification - A New Bandit Framework for Games, Aurélien Garivier, Emilie Kaufmann, Wouter M. Koolen, COLT 2016, New-York, United States, 1028-1050
		⇣	⇣	On Explore-Then-Commit strategies, Aurélien Garivier, Tor Lattimore, Emilie Kaufmann, NIPS 2016, Barcelona, Spain, 784-792
		⇣	⇣	Optimal Best Arm Identification with Fixed Confidence, Aurélien Garivier, Emilie Kaufmann, COLT 2016, New York, United States, 998-1027
		⇣	⇣	On the Complexity of A/B Testing, Emilie Kaufmann ■, Olivier Cappé, Aurélien Garivier, COLT 2014, Barcelona, Spain, 461-481
		⇣		Thompson sampling for one-dimensional exponential family bandits, Nathaniel Korda, Emilie Kaufmann, Rémi Munos, Advances in Neural Information Processing Systems 2013, United States
			⇣	Information Complexity in Bandit Subset Selection, Emilie Kaufmann, Shivaram Kalyanakrishnan, COLT 2013, 228-251
			⇣	Thompson Sampling for 1-Dimensional Exponential Family Bandits, Nathaniel Korda, Emilie Kaufmann, Rémi Munos, NIPS 2013, 1448-1456
		⇣		Thompson Sampling: An Asymptotically Optimal Finite Time Analysis, Emilie Kaufmann, Nathaniel Korda, Rémi Munos, ALT 2012 - International Conference on Algorithmic Learning Theory 2012, Lyon, France

Journal articles

		⇣	⇣	A spectral algorithm with additive clustering for the recovery of overlapping communities in networks, Emilie Kaufmann, Thomas Bonald ■, Marc Lelarge ■, Theoretical Computer Science 2018, 3-26
		⇣		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, 1:1-1:42

Reports

		⇣	⇣	Sequential Test for the Lowest Mean - From Thompson to Murphy Sampling, Emilie Kaufmann, Wouter M. Koolen, Aurélien Garivier, 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

Misc

		⇣		Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals, Emilie Kaufmann, Wouter Koolen, 2018
		⇣		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
		⇣		Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits, Alexander Luedtke, Emilie Kaufmann, Antoine Chambaz, 2017