no code implementations • 6 Mar 2024 • Antoine Scheid, Daniil Tiapkin, Etienne Boursier, Aymeric Capitaine, El Mahdi El Mhamdi, Eric Moulines, Michael I. Jordan, Alain Durmus
This work considers a repeated principal-agent bandit game, where the principal can only interact with her environment through the agent.
1 code implementation • 19 Jan 2024 • Etienne Boursier, Nicolas Flammarion
Training neural networks with first order optimisation methods is at the core of the empirical success of deep learning.
no code implementations • 19 Oct 2023 • Alex Barbier-Chebbah, Christian L. Vestergaard, Jean-Baptiste Masson, Etienne Boursier
Built on this principle, we propose a new class of bandit algorithms that maximize an approximation to the information of a key variable within the system.
no code implementations • 31 May 2023 • Hugo Richard, Etienne Boursier, Vianney Perchet
This motivates the harder, asynchronous multiplayer bandits problem, which was first tackled with an explore-then-commit (ETC) algorithm (see Dakdouk, 2022), with a regret upper-bound in $\mathcal{O}(T^{\frac{2}{3}})$.
no code implementations • 2 Mar 2023 • Oğuz Kaan Yuksel, Etienne Boursier, Nicolas Flammarion
In particular, model-agnostic methods look for initialisation points from which gradient descent quickly adapts to any new task.
no code implementations • 29 Nov 2022 • Etienne Boursier, Vianney Perchet
Due mostly to its application to cognitive radio networks, multiplayer bandits gained a lot of interest in the last decade.
1 code implementation • 2 Jun 2022 • Etienne Boursier, Loucas Pillaud-Vivien, Nicolas Flammarion
The training of neural networks by gradient descent methods is a cornerstone of the deep learning revolution.
1 code implementation • 14 Feb 2022 • Etienne Boursier, Mikhail Konobeev, Nicolas Flammarion
Multi-task learning leverages structural similarities between multiple tasks to learn despite very few samples.
no code implementations • NeurIPS 2021 • Flore Sentenac, Etienne Boursier, Vianney Perchet
In the centralized case, the number of accumulated packets remains bounded (i. e., the system is \textit{stable}) as long as the ratio between service rates and arrival rates is larger than $1$.
1 code implementation • NeurIPS 2021 • Etienne Boursier, Tristan Garrec, Vianney Perchet, Marco Scarsini
If she accepts the proposal, she is busy for the duration of the task and obtains a reward that depends on the task duration.
no code implementations • 20 Jul 2020 • Etienne Boursier, Vianney Perchet, Marco Scarsini
In the simple uni-dimensional and static setting, beliefs about the quality are known to converge to its true value.
no code implementations • NeurIPS 2020 • Pierre Perrault, Etienne Boursier, Vianney Perchet, Michal Valko
In CMAB, the question of the existence of an efficient policy with an optimal asymptotic regret (up to a factor poly-logarithmic with the action size) is still open for many families of distributions, including mutually independent outcomes, and more generally the multivariate sub-Gaussian family.
no code implementations • 4 Feb 2020 • Etienne Boursier, Vianney Perchet
We provide the first algorithm robust to selfish players (a. k. a.
1 code implementation • 27 May 2019 • Etienne Boursier, Vianney Perchet
Strategic information is valuable either by remaining private (for instance if it is sensitive) or, on the other hand, by being used publicly to increase some utility.
no code implementations • 4 Feb 2019 • Etienne Boursier, Emilie Kaufmann, Abbas Mehrabian, Vianney Perchet
We study a multiplayer stochastic multi-armed bandit problem in which players cannot communicate, and if two or more players pull the same arm, a collision occurs and the involved players receive zero reward.
1 code implementation • NeurIPS 2019 • Etienne Boursier, Vianney Perchet
Motivated by cognitive radio networks, we consider the stochastic multiplayer multi-armed bandit problem, where several players pull arms simultaneously and collisions occur if one of them is pulled by several players at the same stage.