Learning, Games and Economics
Professor at CREST, ENSAE
Vianney Perchet is a professor at the Centre de recherche en économie et statistique (CREST) at the ENSAE since october 2019. Mainly focusing on the interplay between machine learning and game theory, his themes of research are at the junction of mathematics, computer science and economics. The spectrum of his interest ranges from pure theory (say, optimal rates of convergence of algorithms) to pure applications (modeling user behavior, optimisation of recommender systems, etc.) He is also part-time principal researcher in the Criteo AI Lab, in Paris, working on efficient exploration in recommender systems.
News
1 Paper @ EC'24
Congratulations to Atulya !!
1 Paper @ COLT'24
Congratulations to Vivien and Charles !!
2 Papers @ ICML'24
Congratulations to Ziyad and Hafedh !!
last publications
Non-clairvoyant Scheduling with Partial Predictions
Ziyad Benomar, Vianney Perchet
Published at ICML’24
The non-clairvoyant scheduling problem has gained new interest within learning-augmented algorithms, where the decision-maker is equipped with predictions without any quality guarantees. In practical settings, access to predictions may be reduced to specific instances, due to cost or data limitations. Our investigation focuses on scenarios where predictions for only B job sizes out of n are available to the algorithm. We first establish near-optimal lower bounds and algorithms in the case of perfect predictions. Subsequently, we present a learning-augmented algorithm satisfying the robustness, consistency, and smoothness criteria, and revealing a novel tradeoff between consistency and smoothness inherent in the scenario with a restricted number of predictions.
@article{benomar2024non,
title={Non-clairvoyant Scheduling with Partial Predictions},
author={Benomar, Ziyad and Perchet, Vianney},
journal={arXiv preprint arXiv:2405.01013},
year={2024} }
Trading-off price for data quality to achieve fair online allocation
Mathieu Molina, Nicolas Gast, Patrick Loiseau, Vianney Perchet
Published at NeurIPS’23
We consider the problem of online allocation subject to a long-term fairness penalty. Contrary to existing works, however, we do not assume that the decision-maker observes the protected attributes—which is often unrealistic in practice. Instead they can purchase data that help estimate them from sources of different quality; and hence reduce the fairness penalty at some cost. We model this problem as a multi-armed bandit problem where each arm corresponds to the choice of a data source, coupled with the fair online allocation problem. We propose an algorithm that jointly solves both problems and show that it has a regret bounded by $\mathcal{O}(\sqrt{T})$. A key difficulty is that the rewards received by selecting a source are correlated by the fairness penalty, which leads to a need for randomization (despite a stochastic setting). Our algorithm takes into account contextual information available before the source selection, and can adapt to many different fairness notions.
title={Trading-off price for data quality to achieve fair online allocation},
author={Molina, Mathieu and Gast, Nicolas and Loiseau, Patrick and Perchet, Vianney},
journal={Advances in Neural Information Processing Systems},
volume={36},
pages={40096–40139},
year={2023} }
Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication
Hugo Richard, Etienne Boursier, Vianney Perchet
Published at AISTATS’24
Multiplayer bandits have recently garnered significant attention due to their relevance in cognitive radio networks. While the existing body of literature predominantly focuses on synchronous players, real-world radio networks, such as those in IoT applications, often feature asynchronous (i.e., randomly activated) devices. This highlights the need for addressing the more challenging asynchronous multiplayer bandits problem. Our first result shows that a natural extension of UCB achieves a minimax regret of $\mathcal{O}(\sqrt{T\log(T)})$ in the centralized setting. More significantly, we introduce Cautious Greedy, which uses $\mathcal{O}(log(T))$ communications and whose instance-dependent regret is constant if the optimal policy assigns at least one player to each arm (a situation proven to occur when arm means are sufficiently close). Otherwise, the regret is, as usual, $log(T))$ times the sum of some inverse sub-optimality gaps. We substantiate the optimality of Cautious Greedy through lower-bound analysis based on data-dependent terms. Therefore, we establish a strong baseline for asynchronous multiplayer bandits, at least with $\mathcal{O}(\log(T))$ communications
@InProceedings{pmlr-v238-richard24a,
title = { Constant or Logarithmic Regret in Asynchronous Multiplayer Bandits with Limited Communication },
author ={Richard, Hugo and Boursier, Etienne and Perchet, Vianney},
booktitle ={Proceedings of The 27th International Conference on Artificial Intelligence and Statistics},
pages={388–396},
year={2024},
editor={Dasgupta, Sanjoy and Mandt, Stephan and Li, Yingzhen},
volume={238},
series={Proceedings of Machine Learning Research},}
NEXT EVENTS
Mediterranean Game Theory Symposium
Games and Artificial Intelligence Multidisciplinary Summer School 2024
workshop Learning in Games
Projects
Internships
Ph. D
Post Doc
Teaching
Auctions & Matching (3A)
We will first introduce the general concept of mechanism design, and especially auctions (1st/2nd price, revenue maximizing) and (stable) matchings that are or can be used in practice. The main questions are their approximation, optimisation and learning based on past data in a dynamical setting. We will introduce and study the main classical tools with a specific focus on prophet inequalities and secretary problems, but also quick reminders on statistical theory, multi-armed bandits and online algorithms.
Advanced Machine Learning (3A)
This course presents the mathematical foundations of machine learning, with a specific focus on binary classification and its statistical complexity and properties. The major algorithms and techniques (SVM, neural nets, boosting) will also be presented and discussed.
They are going to be further analyzed and implemented in practical sessions.
Introduction to Machine Learning (2A)
This course is composed of 10 small and independent lectures that each focuses on a different concept, algorithm or techniques of machine learning. At the end of the course, the students will have a broad knowledge on what is possible to do, and how, with machine learning.
Algorithms are going to be implemented during the practical sessions.
Media Appearances
Contact me:
Vianney Perchet
5 Avenue Le Chatelier, 91120 Palaiseau – FRANCE
vianney.perchet(at)normalesup.org
vianney(at)ensae.fr
v.perchet(at)criteo.com