Pierre Perrault

According to our database, Pierre Perrault authored at least 16 papers between 2019 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

A New Bound on the Cumulant Generating Function of Dirichlet Processes.

Pierre Perrault, Denis Belomestny, Pierre Ménard, Éric Moulines, Alexey Naumov, Daniil Tiapkin, Michal Valko

CoRR, 2024

Limitations of (Procrustes) Alignment in Assessing Multi-Person Human Pose and Shape Estimation.

Drazic Martin, Pierre Perrault

CoRR, 2024

Demonstration-Regularized RL.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Model-free Posterior Sampling via Learning Rate Randomization.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Michal Valko, Pierre Ménard

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Fast Rates for Maximum Entropy Exploration.

Daniil Tiapkin, Denis Belomestny, Daniele Calandriello, Eric Moulines, Rémi Munos, Alexey Naumov, Pierre Perrault, Yunhao Tang, Michal Valko, Pierre Ménard

Proceedings of the International Conference on Machine Learning, 2023

2022

When Combinatorial Thompson Sampling meets Approximation Regret.

Pierre Perrault

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

On the Approximation Relationship between Optimizing Ratio of Submodular (RS) and Difference of Submodular (DS) Functions.

Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko

CoRR, 2021

Online A-Optimal Design and Active Linear Regression.

Xavier Fontaine, Pierre Perrault, Michal Valko, Vianney Perchet

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Efficient Learning in Stochastic Combinatorial Semi-Bandits. (Apprentissage Efficient dans les Problèmes de Semi-Bandits Stochastiques Combinatoires).

Pierre Perrault

PhD thesis, 2020

Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits.

Pierre Perrault, Etienne Boursier, Michal Valko, Vianney Perchet

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Budgeted Online Influence Maximization.

Pierre Perrault, Jennifer Healey, Zheng Wen, Michal Valko

Proceedings of the 37th International Conference on Machine Learning, 2020

Covariance-adapting algorithm for semi-bandits with application to sparse outcomes.

Pierre Perrault, Michal Valko, Vianney Perchet

Proceedings of the Conference on Learning Theory, 2020

2019

Active Linear Regression.

Xavier Fontaine, Pierre Perrault, Vianney Perchet

CoRR, 2019

Exploiting Structure of Uncertainty for Efficient Combinatorial Semi-Bandits.

Pierre Perrault, Vianney Perchet, Michal Valko

CoRR, 2019

Exploiting structure of uncertainty for efficient matroid semi-bandits.

Pierre Perrault, Vianney Perchet, Michal Valko

Proceedings of the 36th International Conference on Machine Learning, 2019

Finding the bandit in a graph: Sequential search-and-stop.

Pierre Perrault, Vianney Perchet, Michal Valko

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019