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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
Algorithmic Learning Theory: 23rd International Conference, ALT 2012, Lyon …, 2012
6692012
On the complexity of best arm identification in multi-armed bandit models
E Kaufmann, O Cappé, A Garivier
Journal of Machine Learning Research 17, 1-42, 2016
4462016
On Bayesian upper confidence bounds for bandit problems
E Kaufmann, O Cappé, A Garivier
Artificial intelligence and statistics, 592-600, 2012
3922012
Optimal best arm identification with fixed confidence
A Garivier, E Kaufmann
Conference on Learning Theory, 998-1027, 2016
2512016
Information complexity in bandit subset selection
E Kaufmann, S Kalyanakrishnan
Conference on Learning Theory, 228-251, 2013
1692013
Thompson sampling for 1-dimensional exponential family bandits
N Korda, E Kaufmann, R Munos
Advances in neural information processing systems 26, 2013
1532013
Machine learning applications in drug development
C Réda, E Kaufmann, A Delahaye-Duriez
Computational and structural biotechnology journal 18, 241-252, 2020
1342020
Multi-player bandits revisited
L Besson, E Kaufmann
Algorithmic Learning Theory, 56-92, 2018
952018
Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings
R Bonnefoi, L Besson, C Moy, E Kaufmann, J Palicot
Cognitive Radio Oriented Wireless Networks: 12th International Conference …, 2018
902018
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems 29, 2016
862016
What doubling tricks can and can't do for multi-armed bandits
L Besson, E Kaufmann
arXiv preprint arXiv:1803.06971, 2018
732018
Mixture martingales revisited with applications to sequential tests and confidence intervals
E Kaufmann, WM Koolen
The Journal of Machine Learning Research 22 (1), 11140-11183, 2021
722021
Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited
OD Domingues, P Ménard, E Kaufmann, M Valko
Algorithmic Learning Theory, 578-598, 2021
632021
On the complexity of A/B testing
E Kaufmann, O Cappé, A Garivier
Conference on Learning Theory, 461-481, 2014
602014
A practical algorithm for multiplayer bandits when arm means vary among players
A Mehrabian, E Boursier, E Kaufmann, V Perchet
International Conference on Artificial Intelligence and Statistics, 1211-1221, 2020
57*2020
Monte-Carlo tree search by best arm identification
E Kaufmann, WM Koolen
Advances in Neural Information Processing Systems 30, 2017
482017
Adaptive reward-free exploration
E Kaufmann, P Ménard, OD Domingues, A Jonsson, E Leurent, M Valko
Algorithmic Learning Theory, 865-891, 2021
452021
A spectral algorithm with additive clustering for the recovery of overlapping communities in networks
E Kaufmann, T Bonald, M Lelarge
Theoretical Computer Science 742, 3-26, 2018
44*2018
On Bayesian index policies for sequential resource allocation
E Kaufmann
The Annals of Statistics 46 (2), 842-865, 2018
392018
On multi-armed bandit designs for dose-finding clinical trials
M Aziz, E Kaufmann, MK Riviere
The Journal of Machine Learning Research 22 (1), 686-723, 2021
372021
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