Emilie Kaufmann
Emilie Kaufmann
CNRS & Univ. Lille (CRIStAL)
Verified email at inria.fr - Homepage
Title
Cited by
Cited by
Year
Thompson sampling: An asymptotically optimal finite-time analysis
E Kaufmann, N Korda, R Munos
International conference on algorithmic learning theory, 199-213, 2012
3922012
On Bayesian upper confidence bounds for bandit problems
E Kaufmann, O Cappé, A Garivier
Artificial intelligence and statistics, 592-600, 2012
2512012
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
2012016
Information complexity in bandit subset selection
E Kaufmann, S Kalyanakrishnan
Conference on Learning Theory, 228-251, 2013
932013
Thompson sampling for 1-dimensional exponential family bandits
N Korda, E Kaufmann, R Munos
Advances in neural information processing systems, 1448-1456, 2013
932013
Optimal best arm identification with fixed confidence
A Garivier, E Kaufmann
Conference on Learning Theory, 998-1027, 2016
782016
Multi-player bandits revisited
L Besson, E Kaufmann
arXiv preprint arXiv:1711.02317, 2017
362017
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems, 784-792, 2016
332016
On Bayesian index policies for sequential resource allocation
E Kaufmann
arXiv preprint arXiv:1601.01190, 2016
322016
On the complexity of a/b testing
E Kaufmann, O Cappé, A Garivier
Conference on Learning Theory, 461-481, 2014
302014
Analyse de stratégies Bayésiennes et fréquentistes pour l'allocation séquentielle de ressources
E Kaufmann
Paris, ENST, 2014
262014
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
252017
Maximin action identification: A new bandit framework for games
A Garivier, E Kaufmann, WM Koolen
Conference on Learning Theory, 1028-1050, 2016
242016
Monte-carlo tree search by best arm identification
E Kaufmann, WM Koolen
Advances in Neural Information Processing Systems, 4897-4906, 2017
182017
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
17*2016
Mixture martingales revisited with applications to sequential tests and confidence intervals
E Kaufmann, W Koolen
arXiv preprint arXiv:1811.11419, 2018
152018
What doubling tricks can and can't do for multi-armed bandits
L Besson, E Kaufmann
arXiv preprint arXiv:1803.06971, 2018
132018
Learning the distribution with largest mean: two bandit frameworks
E Kaufmann, A Garivier
ESAIM: Proceedings and surveys 60, 114-131, 2017
122017
Corrupt bandits for preserving local privacy
P Gajane, T Urvoy, E Kaufmann
arXiv preprint arXiv:1708.05033, 2017
112017
A practical algorithm for multiplayer bandits when arm means vary among players
E Boursier, E Kaufmann, A Mehrabian, V Perchet
102019
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