Emilie Kaufmann
Emilie Kaufmann
CNRS & Univ. Lille (CRIStAL)
Adresse e-mail validée de inria.fr - Page d'accueil
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Thompson sampling: An asymptotically optimal finite-time analysis
E Kaufmann, N Korda, R Munos
International conference on algorithmic learning theory, 199-213, 2012
5282012
On Bayesian upper confidence bounds for bandit problems
E Kaufmann, O Cappé, A Garivier
Artificial intelligence and statistics, 592-600, 2012
3142012
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
3012016
Optimal best arm identification with fixed confidence
A Garivier, E Kaufmann
Conference on Learning Theory, 998-1027, 2016
1252016
Thompson sampling for 1-dimensional exponential family bandits
N Korda, E Kaufmann, R Munos
arXiv preprint arXiv:1307.3400, 2013
1242013
Information complexity in bandit subset selection
E Kaufmann, S Kalyanakrishnan
Conference on Learning Theory, 228-251, 2013
1222013
Multi-player bandits revisited
L Besson, E Kaufmann
Algorithmic Learning Theory, 56-92, 2018
632018
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems 29, 784-792, 2016
492016
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
472017
On Bayesian index policies for sequential resource allocation
E Kaufmann
arXiv preprint arXiv:1601.01190, 2016
452016
On the complexity of A/B testing
E Kaufmann, O Cappé, A Garivier
Conference on Learning Theory, 461-481, 2014
412014
Mixture martingales revisited with applications to sequential tests and confidence intervals
E Kaufmann, W Koolen
arXiv preprint arXiv:1811.11419, 2018
382018
What doubling tricks can and can't do for multi-armed bandits
L Besson, E Kaufmann
arXiv preprint arXiv:1803.06971, 2018
362018
Maximin action identification: A new bandit framework for games
A Garivier, E Kaufmann, WM Koolen
Conference on Learning Theory, 1028-1050, 2016
332016
Monte-Carlo tree search by best arm identification
E Kaufmann, W Koolen
arXiv preprint arXiv:1706.02986, 2017
322017
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
30*2016
Analyse de stratégies bayésiennes et fréquentistes pour l'allocation séquentielle de ressources
E Kaufmann
Paris, ENST, 2014
302014
Machine learning applications in drug development
C Réda, E Kaufmann, A Delahaye-Duriez
Computational and structural biotechnology journal 18, 241-252, 2020
292020
A practical algorithm for multiplayer bandits when arm means vary among players
E Boursier, E Kaufmann, A Mehrabian, V Perchet
22*2020
Corrupt bandits for preserving local privacy
P Gajane, T Urvoy, E Kaufmann
Algorithmic Learning Theory, 387-412, 2018
212018
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