Search - Archive ouverte HAL Access content directly

Filter your results

54 Results
Image document

Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling

Cindy Trinh , Emilie Kaufmann , Claire Vernade , Richard Combes
ALT 2020 - 31st International Conference on Algorithmic Learning Theory, Feb 2020, San Diego, United States. pp.1 - 28
Conference papers hal-02396943v2
Image document

Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling

Emilie Kaufmann , Wouter Koolen , Aurélien Garivier
Advances in Neural Information Processing Systems (NIPS), Dec 2018, Montréal, Canada
Conference papers hal-01804581v1
Image document

Corrupt Bandits for Preserving Local Privacy

Pratik Gajane , Tanguy Urvoy , Emilie Kaufmann
ALT 2018 - Algorithmic Learning Theory, Apr 2018, Lanzarote, Spain
Conference papers hal-01757297v1
Image document

Adaptive black-box optimization got easier: HCT only needs local smoothness

Xuedong Shang , Emilie Kaufmann , Michal Valko
European Workshop on Reinforcement Learning, Oct 2018, Lille, France
Conference papers hal-01874637v1

Information Complexity in Bandit Subset Selection

Emilie Kaufmann , Shivaram Kalyanakrishnan
Conference On Learning Theory, Jun 2013, Princeton, United States
Conference papers hal-02288406v1
Image document

On Multi-Armed Bandit Designs for Dose-Finding Trials

Maryam Aziz , Emilie Kaufmann , Marie-Karelle Riviere
Journal of Machine Learning Research, 2021
Journal articles hal-02533297v1
Image document

Kernel-based reinforcement Learning: A finite-time analysis

Omar D Domingues , Pierre Ménard , Matteo Pirotta , Emilie Kaufmann , Michal Valko
International Conference on Machine Learning, Jul 2021, Vienna / Virtual, Austria
Conference papers hal-02541790v2

Thompson Sampling : an asymptotically optimal finite time analysis

Emilie Kaufmann , Nathaniel Korda , Rémi Munos
International Conference on Algorithmic Learning Theory, Nov 2012, Lyon, France. pp.199-213
Conference papers hal-02286442v1
Image document

On the Complexity of Best Arm Identification in Multi-Armed Bandit Models

Emilie Kaufmann , Olivier Cappé , Aurélien Garivier
Journal of Machine Learning Research, 2016, 17, pp.1-42
Journal articles hal-01024894v2
Image document

Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs

Andrea Tirinzoni , Aymen Al-Marjani , Emilie Kaufmann
EWRL 2022 - European Workshop on Reinforcement Learning, Sep 2022, Milan, Italy
Conference papers hal-03767412v1
Image document

Fixed-confidence guarantees for Bayesian best-arm identification

Xuedong Shang , Rianne de Heide , Emilie Kaufmann , Pierre Ménard , Michal Valko
International Conference on Artificial Intelligence and Statistics, 2020, Palermo, Italy
Conference papers hal-02330187v2
Image document

Fast active learning for pure exploration in reinforcement learning

Pierre Ménard , Omar Darwiche Domingues , Emilie Kaufmann , Anders Jonsson , Edouard Leurent , et al.
International Conference on Machine Learning, Jul 2021, Vienna, Austria
Conference papers hal-02906985v3
Image document

A Spectral Algorithm with Additive Clustering for the Recovery of Overlapping Communities in Networks

Emilie Kaufmann , Thomas Bonald , Marc Lelarge
Theoretical Computer Science, 2018, 742, pp.3-26. ⟨10.1016/j.tcs.2017.12.028⟩
Journal articles hal-01163147v3
Image document

Asymptotically Optimal Algorithms for Budgeted Multiple Play Bandits

Alexander Luedtke , Emilie Kaufmann , Antoine Chambaz
Machine Learning, 2019, 108 (11), pp.1919-1949. ⟨10.1007/s10994-019-05799-x⟩
Journal articles hal-01338733v3
Image document

Maximin Action Identification: A New Bandit Framework for Games

Aurélien Garivier , Emilie Kaufmann , Wouter M. Koolen
29th Annual Conference on Learning Theory (COLT), Jun 2016, New-York, United States
Conference papers hal-01273842v2
Image document

On Explore-Then-Commit Strategies

Aurélien Garivier , Emilie Kaufmann , Tor Lattimore
NIPS, Dec 2016, Barcelona, Spain
Conference papers hal-01322906v2
Image document

A Practical Algorithm for Multiplayer Bandits when Arm Means Vary Among Players

Etienne Boursier , Emilie Kaufmann , Abbas Mehrabian , Vianney Perchet
AISTATS 2020 - 23rd International Conference on Artificial Intelligence and Statistics, Aug 2020, Palermo, Italy
Conference papers hal-02006069v3
Image document

Thompson sampling for one-dimensional exponential family bandits

Nathaniel Korda , Emilie Kaufmann , Rémi Munos
Advances in Neural Information Processing Systems, 2013, United States
Conference papers hal-00923683v1
Image document

Episodic reinforcement learning in finite MDPs: Minimax lower bounds revisited

Omar Darwiche Domingues , Pierre Ménard , Emilie Kaufmann , Michal Valko
Algorithmic Learning Theory, Mar 2021, Paris / Virtual, France
Conference papers hal-03289004v1
Image document

Multi-Armed Bandit Learning in IoT Networks: Learning helps even in non-stationary settings

Rémi Bonnefoi , Lilian Besson , Christophe Moy , Emilie Kaufmann , Jacques Palicot
CROWNCOM 2017 - 12th EAI International Conference on Cognitive Radio Oriented Wireless Networks, Sep 2017, Lisbon, Portugal. pp.173-185, ⟨10.1007/978-3-319-76207-4_15⟩
Conference papers hal-01575419v2
Image document

On Bayesian index policies for sequential resource allocation

Emilie Kaufmann
Annals of Statistics, 2018, 46 (2), pp.842-865. ⟨10.1214/17-AOS1569⟩
Journal articles hal-01251606v3
Image document

Near-Optimal Collaborative Learning in Bandits

Clémence Réda , Sattar Vakili , Emilie Kaufmann
NeurIPS 2022 - 36th Conference on Neural Information Processing System, Dec 2022, New Orleans, United States
Conference papers hal-03825099v1
Image document

A simple dynamic bandit algorithm for hyper-parameter tuning

Xuedong Shang , Emilie Kaufmann , Michal Valko
Workshop on Automated Machine Learning at International Conference on Machine Learning, AutoML@ICML 2019 - 6th ICML Workshop on Automated Machine Learning, Jun 2019, Long Beach, United States
Conference papers hal-02145200v1

Thompson Sampling for one-dimensial exponential family bandits

Nathaniel Korda , Emilie Kaufmann , Rémi Munos
NIPS 2013 - Neural Information Processing Systems Conference, Dec 2013, Lake Tahoe, United States
Conference papers hal-02288407v1

Thompson Sampling: An Asymptotically Optimal Finite Time Analysis

Emilie Kaufmann , Nathaniel Korda , Rémi Munos
ALT 2012 - International Conference on Algorithmic Learning Theory, Oct 2012, Lyon, France. pp.199-213, ⟨10.1007/978-3-642-34106-9_18⟩
Conference papers hal-00830033v1
Image document

Optimal Thompson Sampling strategies for support-aware CVaR bandits

Dorian Baudry , Romain Gautron , Emilie Kaufmann , Odalric-Ambrym Maillard
38th International Conference on Machine Learning, Jul 2021, Virtual, United States
Conference papers hal-03447244v1
Image document

Top Two Algorithms Revisited

Marc Jourdan , Rémy Degenne , Dorian Baudry , Rianne de Heide , Emilie Kaufmann
NeurIPS 2022 - 36th Conference on Neural Information Processing System, Nov 2022, New Orleans, United States
Conference papers hal-03825103v1
Image document

Efficient Algorithms for Extreme Bandits

Dorian Baudry , Yoan Russac , Emilie Kaufmann
International conferenece on Articifial Intelligence and Statistics (AISTATS), Mar 2022, Virtual Conference, Spain
Conference papers hal-03741302v1
Image document

Pure Exploration in Infinitely-Armed Bandit Models with Fixed-Confidence

Maryam Aziz , Jesse Anderton , Emilie Kaufmann , Javed Aslam
ALT 2018 - Algorithmic Learning Theory, Apr 2018, Lanzarote, Spain
Conference papers hal-01729969v1
Image document

Optimal Best Arm Identification with Fixed Confidence

Aurélien Garivier , Emilie Kaufmann
29th Annual Conference on Learning Theory (COLT), Jun 2016, New York, United States
Conference papers hal-01273838v2