Emilie Kaufmann
Emilie Kaufmann
CNRS & Univ. Lille (CRIStAL)
Bestätigte E-Mail-Adresse bei inria.fr - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Thompson sampling: An asymptotically optimal finite-time analysis
E Kaufmann, N Korda, R Munos
International conference on algorithmic learning theory, 199-213, 2012
4392012
On Bayesian upper confidence bounds for bandit problems
E Kaufmann, O Cappé, A Garivier
Artificial intelligence and statistics, 592-600, 2012
2742012
On the complexity of best-arm identification in multi-armed bandit models
E Kaufmann, O Cappé, A Garivier
The Journal of Machine Learning Research 17 (1), 1-42, 2016
2392016
Thompson sampling for 1-dimensional exponential family bandits
N Korda, E Kaufmann, R Munos
Advances in neural information processing systems, 1448-1456, 2013
1012013
Information complexity in bandit subset selection
E Kaufmann, S Kalyanakrishnan
Conference on Learning Theory, 228-251, 2013
982013
Optimal best arm identification with fixed confidence
A Garivier, E Kaufmann
Conference on Learning Theory, 998-1027, 2016
942016
Multi-player bandits revisited
L Besson, E Kaufmann
Algorithmic Learning Theory, 56-92, 2018
482018
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems, 784-792, 2016
392016
On the complexity of A/B testing
E Kaufmann, O Cappé, A Garivier
Conference on Learning Theory, 461-481, 2014
362014
Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings
R Bonnefoi, L Besson, C Moy, E Kaufmann, J Palicot
International Conference on Cognitive Radio Oriented Wireless Networks, 173-185, 2017
342017
On Bayesian index policies for sequential resource allocation
E Kaufmann
arXiv preprint arXiv:1601.01190, 2016
332016
Analyse de stratégies Bayésiennes et fréquentistes pour l'allocation séquentielle de ressources
E Kaufmann
Paris, ENST, 2014
272014
Maximin action identification: A new bandit framework for games
A Garivier, E Kaufmann, WM Koolen
Conference on Learning Theory, 1028-1050, 2016
252016
Mixture martingales revisited with applications to sequential tests and confidence intervals
E Kaufmann, W Koolen
arXiv preprint arXiv:1811.11419, 2018
232018
Monte-Carlo tree search by best arm identification
E Kaufmann, WM Koolen
Advances in Neural Information Processing Systems, 4897-4906, 2017
222017
A spectral algorithm with additive clustering for the recovery of overlapping communities in networks
E Kaufmann, T Bonald, M Lelarge
International Conference on Algorithmic Learning Theory, 355-370, 2016
20*2016
What doubling tricks can and can't do for multi-armed bandits
L Besson, E Kaufmann
arXiv preprint arXiv:1803.06971, 2018
172018
Learning the distribution with largest mean: two bandit frameworks
E Kaufmann, A Garivier
ESAIM: Proceedings and surveys 60, 114-131, 2017
152017
Corrupt bandits for preserving local privacy
P Gajane, T Urvoy, E Kaufmann
Algorithmic Learning Theory, 387-412, 2018
142018
A practical algorithm for multiplayer bandits when arm means vary among players
E Boursier, V Perchet, E Kaufmann, A Mehrabian
arXiv preprint arXiv:1902.01239, 2019
12*2019
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