Emilie Kaufmann
Emilie Kaufmann
CNRS & Univ. Lille (CRIStAL)
Verified email at - Homepage
Cited by
Cited by
Thompson sampling: An asymptotically optimal finite-time analysis
E Kaufmann, N Korda, R Munos
International conference on algorithmic learning theory, 199-213, 2012
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
On Bayesian upper confidence bounds for bandit problems
E Kaufmann, O Cappé, A Garivier
Artificial intelligence and statistics, 592-600, 2012
Optimal best arm identification with fixed confidence
A Garivier, E Kaufmann
Conference on Learning Theory, 998-1027, 2016
Information complexity in bandit subset selection
E Kaufmann, S Kalyanakrishnan
Conference on Learning Theory, 228-251, 2013
Thompson sampling for 1-dimensional exponential family bandits
N Korda, E Kaufmann, R Munos
Advances in neural information processing systems 26, 2013
Machine learning applications in drug development
C Réda, E Kaufmann, A Delahaye-Duriez
Computational and structural biotechnology journal 18, 241-252, 2020
Multi-player bandits revisited
L Besson, E Kaufmann
Algorithmic Learning Theory, 56-92, 2018
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
On explore-then-commit strategies
A Garivier, T Lattimore, E Kaufmann
Advances in Neural Information Processing Systems 29, 2016
What doubling tricks can and can't do for multi-armed bandits
L Besson, E Kaufmann
arXiv preprint arXiv:1803.06971, 2018
Mixture Martingales Revisited with Applications to Sequential Tests and Confidence Intervals.
E Kaufmann, WM Koolen
J. Mach. Learn. Res. 22, 246:1-246:44, 2021
On Bayesian index policies for sequential resource allocation
E Kaufmann
The Annals of Statistics 46 (2), 842-865, 2018
On the complexity of A/B testing
E Kaufmann, O Cappé, A Garivier
Conference on Learning Theory, 461-481, 2014
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
Monte-Carlo tree search by best arm identification
E Kaufmann, WM Koolen
Advances in Neural Information Processing Systems 30, 2017
Episodic reinforcement learning in finite mdps: Minimax lower bounds revisited
OD Domingues, P Ménard, E Kaufmann, M Valko
Algorithmic Learning Theory, 578-598, 2021
Adaptive reward-free exploration
E Kaufmann, P Ménard, OD Domingues, A Jonsson, E Leurent, M Valko
Algorithmic Learning Theory, 865-891, 2021
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
Fast active learning for pure exploration in reinforcement learning
P Ménard, OD Domingues, A Jonsson, E Kaufmann, E Leurent, M Valko
International Conference on Machine Learning, 7599-7608, 2021
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