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Yann Chevaleyre

17 accepted papers

2025

Improving Diversity in Language Models: When Temperature Fails, Change the Loss

Alexandre Verine, Florian Le Bronnec, Kunhao Zheng, Alexandre Allauzen, Yann Chevaleyre, benjamin negrevergne
ICML 2025poster

Increasing diversity in language models is a challenging yet essential objective. A common approach is to raise the decoding temperature. In this work, we investigate this approach through a simplistic yet common case to provide insights into why decreasing temperature can improve quality (Precision…

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2025

Unveiling the Role of Randomization in Multiclass Adversarial Classification: Insights from Graph Theory

Lucas Gnecco Heredia, Matteo Sammut, Muni Sreenivas Pydi, Rafael Pinot, benjamin negrevergne, Yann Chevaleyre
AISTATS 2025poster

Randomization as a mean to improve the adversarial robustness of machine learning models has recently attracted significant attention. Unfortunately, much of the theoretical analysis so far has focused on binary classification, providing only limited insights into the more complex multiclass setting…

Cited by 0Source ↗Scholar ↗
2024

Exploring Precision and Recall to assess the quality and diversity of LLMs

Florian Le Bronnec, Alexandre Verine, Benjamin Negrevergne, Yann Chevaleyre, Alexandre Allauzen
ACL 2024long

We introduce a novel evaluation framework for Large Language Models (LLMs) such as Llama-2 and Mistral, focusing on importing Precision and Recall metrics from image generation to text generation. This approach allows for a nuanced assessment of the quality and diversity of generated text without th…

Cited by 15Source ↗PDF ↗code ↗Scholar ↗
2024

Optimal Budgeted Rejection Sampling for Generative Models

Alexandre Verine, Muni Sreenivas Pydi, Benjamin Negrevergne, Yann Chevaleyre
AISTATS 2024poster

Rejection sampling methods have recently been proposed to improve the performance of discriminator-based generative models. However, these methods are only optimal under an unlimited sampling budget, and are usually applied to a generator trained independently of the rejection procedure. We first pr…

Cited by 5Source ↗PDF ↗Scholar ↗
2023

Mediated Uncoupled Learning and Validation with Bregman Divergences: Loss Family with Maximal Generality

Ikko Yamane, Yann Chevaleyre, Takashi Ishida, Florian Yger
AISTATS 2023poster

In mediated uncoupled learning (MU-learning), the goal is to predict an output variable $Y$ given an input variable $X$ as in ordinary supervised learning while the training dataset has no joint samples of $(X, Y)$ but only independent samples of $(X, U)$ and $(U, Y)$ each observed with a mediating…

Cited by 1Source ↗PDF ↗code ↗Scholar ↗
2023

On the Role of Randomization in Adversarially Robust Classification

Lucas Gnecco Heredia, Muni Sreenivas Pydi, Laurent Meunier, benjamin negrevergne, Yann Chevaleyre
NeurIPS 2023spotlight

Deep neural networks are known to be vulnerable to small adversarial perturbations in test data. To defend against adversarial attacks, probabilistic classifiers have been proposed as an alternative to deterministic ones. However, literature has conflicting findings on the effectiveness of probabili…

Cited by 5Source ↗PDF ↗Scholar ↗
2023

Precision-Recall Divergence Optimization for Generative Modeling with GANs and Normalizing Flows

Alexandre Verine, benjamin negrevergne, Muni Sreenivas Pydi, Yann Chevaleyre
NeurIPS 2023poster

Achieving a balance between image quality (precision) and diversity (recall) is a significant challenge in the domain of generative models. Current state-of-the-art models primarily rely on optimizing heuristics, such as the Fr\'echet Inception Distance. While recent developments have introduced pri…

Cited by 11Source ↗PDF ↗Scholar ↗
2022

An $\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits

Geovani Rizk, Igor Colin, Albert Thomas, Rida Laraki, Yann Chevaleyre
NeurIPS 2022accept

We propose the first regret-based approach to the \emph{Graphical Bilinear Bandits} problem, where $n$ agents in a graph play a stochastic bilinear bandit game with each of their neighbors. This setting reveals a combinatorial NP-hard problem that prevents the use of any existing regret-based algori…

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2022

Reactive Stepping for Humanoid Robots using Reinforcement Learning: Application to Standing Push Recovery on the Exoskeleton Atalante

Alexis Duburcq, Fabian Schramm, Guilhem Boéris, Nicolas Bredeche, Yann Chevaleyre
IROS 2022poster

State-of-the-art reinforcement learning is now able to learn versatile locomotion, balancing and push-recovery capabilities for bipedal robots in simulation. Yet, the reality gap has mostly been overlooked and the simulated results hardly transfer to real hardware. Either it is unsuccessful in pract…

Cited by 13Source ↗Scholar ↗
2022

Towards Consistency in Adversarial Classification

Laurent Meunier, Raphael Ettedgui, Rafael Pinot, Yann Chevaleyre, Jamal Atif
NeurIPS 2022accept

In this paper, we study the problem of consistency in the context of adversarial examples. Specifically, we tackle the following question: can surrogate losses still be used as a proxy for minimizing the $0/1$ loss in the presence of an adversary that alters the inputs at test-time? Different from t…

Cited by 10Source ↗PDF ↗Scholar ↗
2021

Best Arm Identification in Graphical Bilinear Bandits

Geovani Rizk, Albert Thomas, Igor Colin, Rida Laraki, Yann Chevaleyre
ICML 2021spotlight

We introduce a new graphical bilinear bandit problem where a learner (or a \emph{central entity}) allocates arms to the nodes of a graph and observes for each edge a noisy bilinear reward representing the interaction between the two end nodes. We study the best arm identification problem in which th…

Cited by 7Source ↗PDF ↗Scholar ↗
2021

Mixed Nash Equilibria in the Adversarial Examples Game

Laurent Meunier, Meyer Scetbon, Rafael B Pinot, Jamal Atif, Yann Chevaleyre
ICML 2021spotlight

This paper tackles the problem of adversarial examples from a game theoretic point of view. We study the open question of the existence of mixed Nash equilibria in the zero-sum game formed by the attacker and the classifier. While previous works usually allow only one player to use randomized strate…

Cited by 38Source ↗PDF ↗Scholar ↗
2021

On Lipschitz Regularization of Convolutional Layers using Toeplitz Matrix Theory

Alexandre Araujo, Benjamin Negrevergne, Yann Chevaleyre, Jamal Atif
AAAI 2021technical

This paper tackles the problem of Lipschitz regularization of Convolutional Neural Networks. Lipschitz regularity is now established as a key property of modern deep learning with implications in training stability, generalization, robustness against adversarial examples, etc. However, computing the…

Cited by 36Source ↗PDF ↗code ↗Scholar ↗
2020

Online Trajectory Planning Through Combined Trajectory Optimization and Function Approximation: Application to the Exoskeleton Atalante

Alexis Duburcq, Yann Chevaleyre, Nicolas Bredeche, Guilhem Boéris
ICRA 2020poster

Autonomous robots require online trajectory planning capability to operate in the real world. Efficient offline trajectory planning methods already exist, but are computationally demanding, preventing their use online. In this paper, we present a novel algorithm called Guided Trajectory Learning tha…

Cited by 23Source ↗Scholar ↗
2019

Interpretable Cascade Classifiers with Abstention

Matthieu Clertant, Nataliya Sokolovska, Yann Chevaleyre, Blaise Hanczar
AISTATS 2019poster

In many prediction tasks such as medical diagnostics, sequential decisions are crucial to provide optimal individual treatment. Budget in real-life applications is always limited, and it can represent any limited resource such as time, money, or side effects of medications. In this contribution, we…

Cited by 10Source ↗PDF ↗Scholar ↗
2018

A Provable Algorithm for Learning Interpretable Scoring Systems

Nataliya Sokolovska, Yann Chevaleyre, Jean-Daniel Zucker
AISTATS 2018poster

Score learning aims at taking advantage of supervised learning to produce interpretable models which facilitate decision making. Scoring systems are simple classification models that let users quickly perform stratification. Ideally, a scoring system is based on simple arithmetic operations, is spar…

Cited by 0Source ↗PDF ↗code ↗Scholar ↗