137 citations · 208 across the 22 of their papers we have counts for
24 papers
On the Complexity of Preference-Based Bandits
Ahmed Ben Yahmed, Marc Abeille, Clément Calauzènes
We study preference-based bandits with general reward function classes, where a learner sequentially selects pairs of arms and observes binary preference feedback governed by the B…
Online matching on stochastic block model
Maria Cherifa, Clément Calauzènes, Vianney Perchet
While online bipartite matching has gained significant attention in recent years, existing analyses in stochastic settings fail to capture the performance of algorithms on heteroge…
Strategic Multi-Armed Bandit Problems Under Debt-Free Reporting
Ahmed Ben Yahmed, Clément Calauzènes, Vianney Perchet
We consider the classical multi-armed bandit problem, but with strategic arms. In this context, each arm is characterized by a bounded support reward distribution and strategically…
Dynamic online matching with budget refills
Maria Cherifa, Clément Calauzènes, Vianney Perchet
Inspired by sequential budgeted allocation problems, we study the online matching problem with budget refills. In this context, we consider an online bipartite graph , w…
Robust Consensus in Ranking Data Analysis: Definitions, Properties and Computational Issues
Morgane Goibert, Clément Calauzènes, Ekhine Irurozki +1
As the issue of robustness in AI systems becomes vital, statistical learning techniques that are reliable even in presence of partly contaminated data have to be developed. Prefere…
Jointly Efficient and Optimal Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Kwang-Sung Jun +1
Logistic Bandits have recently undergone careful scrutiny by virtue of their combined theoretical and practical relevance. This research effort delivered statistically efficient al…
Pure Exploration and Regret Minimization in Matching Bandits
Flore Sentenac, Jialin Yi, Clément Calauzènes +2
Finding an optimal matching in a weighted graph is a standard combinatorial problem. We consider its semi-bandit version where either a pair or a full matching is sampled sequentia…
Regret Bounds for Generalized Linear Bandits under Parameter Drift
Louis Faury, Yoan Russac, Marc Abeille +1
Generalized Linear Bandits (GLBs) are powerful extensions to the Linear Bandit (LB) setting, broadening the benefits of reward parametrization beyond linearity. In this paper we st…
Wasserstein Learning of Determinantal Point Processes
Lucas Anquetil, Mike Gartrell, Alain Rakotomamonjy +2
Determinantal point processes (DPPs) have received significant attention as an elegant probabilistic model for discrete subset selection. Most prior work on DPP learning focuses on…
Learning in repeated auctions
Thomas Nedelec, Clément Calauzènes, Noureddine El Karoui +1
Online auctions are one of the most fundamental facets of the modern economy and power an industry generating hundreds of billions of dollars a year in revenue. Auction theory has…
Real-Time Optimisation for Online Learning in Auctions
Lorenzo Croissant, Marc Abeille, Clément Calauzènes
In display advertising, a small group of sellers and bidders face each other in up to 10 12 auctions a day. In this context, revenue maximisation via monopoly price learning is a h…
Instance-Wise Minimax-Optimal Algorithms for Logistic Bandits
Marc Abeille, Louis Faury, Clément Calauzènes
Logistic Bandits have recently attracted substantial attention, by providing an uncluttered yet challenging framework for understanding the impact of non-linearity in parametrized…
Do Not Mask What You Do Not Need to Mask: a Parser-Free Virtual Try-On
Thibaut Issenhuth, Jérémie Mary, Clément Calauzènes
The 2D virtual try-on task has recently attracted a great interest from the research community, for its direct potential applications in online shopping as well as for its inherent…
Improved Optimistic Algorithms for Logistic Bandits
Louis Faury, Marc Abeille, Clément Calauzènes +1
The generalized linear bandit framework has attracted a lot of attention in recent years by extending the well-understood linear setting and allowing to model richer reward structu…
Embedding models for recommendation under contextual constraints
Syrine Krichene, Mike Gartrell, Clement Calauzenes
Embedding models, which learn latent representations of users and items based on user-item interaction patterns, are a key component of recommendation systems. In many applications…
End-to-End Learning of Geometric Deformations of Feature Maps for Virtual Try-On
Thibaut Issenhuth, Jérémie Mary, Clément Calauzènes
The 2D virtual try-on task has recently attracted a lot of interest from the research community, for its direct potential applications in online shopping as well as for its inheren…
Robust Stackelberg buyers in repeated auctions
Clément Calauzènes, Thomas Nedelec, Vianney Perchet +1
We consider the practical and classical setting where the seller is using an exploration stage to learn the value distributions of the bidders before running a revenue-maximizing a…
Improving Evolutionary Strategies with Generative Neural Networks
Louis Faury, Clement Calauzenes, Olivier Fercoq +1
Evolutionary Strategies (ES) are a popular family of black-box zeroth-order optimization algorithms which rely on search distributions to efficiently optimize a large variety of ob…
Bridging the gap between regret minimization and best arm identification, with application to A/B tests
Rémy Degenne, Thomas Nedelec, Clément Calauzènes +1
State of the art online learning procedures focus either on selecting the best alternative ("best arm identification") or on minimizing the cost (the "regret"). We merge these two…
Thresholding at the monopoly price: an agnostic way to improve bidding strategies in revenue-maximizing auctions
Thomas Nedelec, Marc Abeille, Clément Calauzènes +3
We address the problem of improving bidders' strategies in prior-dependent revenue-maximizing auctions and introduce a simple and generic method to design novel bidding strategies…
Neural Generative Models for Global Optimization with Gradients
Louis Faury, Flavian Vasile, Clément Calauzènes +1
The aim of global optimization is to find the global optimum of arbitrary classes of functions, possibly highly multimodal ones. In this paper we focus on the subproblem of global…
Explicit shading strategies for repeated truthful auctions
Marc Abeille, Clément Calauzènes, Noureddine El Karoui +2
With the increasing use of auctions in online advertising, there has been a large effort to study seller revenue maximization, following Myerson's seminal work, both theoretically…
Offline A/B testing for Recommender Systems
Alexandre Gilotte, Clément Calauzènes, Thomas Nedelec +2
Before A/B testing online a new version of a recommender system, it is usual to perform some offline evaluations on historical data. We focus on evaluation methods that compute an…
Distributed SAGA: Maintaining linear convergence rate with limited communication
Clément Calauzènes, Nicolas Le Roux
In recent years, variance-reducing stochastic methods have shown great practical performance, exhibiting linear convergence rate when other stochastic methods offered a sub-linear…