76 citations · 215 across the 31 of their papers we have counts for
17 papers · 1 filter
Top- Pareto Bandits: Hypervolume Regret for Multi-Objective Slate Selection
Nicolas Gutowski, Fabien Chhel, Alexandre Letard +1
We consider a stochastic multi-objective bandit problem where, at each round, the agent selects a slate of arms and observes their -dimensional reward vectors under semi-ban…
ACT: Agentic Classification Tree
Vincent Grari, Tim Arni, Thibault Laugel +3
When used in high-stakes settings, AI systems are expected to produce decisions that are transparent, interpretable and auditable, a requirement increasingly expected by regulation…
On the Fairness ROAD: Robust Optimization for Adversarial Debiasing
Vincent Grari, Thibault Laugel, Tatsunori Hashimoto +2
In the field of algorithmic fairness, significant attention has been put on group fairness criteria, such as Demographic Parity and Equalized Odds. Nevertheless, these objectives,…
Deep Generative Symbolic Regression with Monte-Carlo-Tree-Search
Pierre-Alexandre Kamienny, Guillaume Lample, Sylvain Lamprier +1
Symbolic regression (SR) is the problem of learning a symbolic expression from numerical data. Recently, deep neural models trained on procedurally-generated synthetic datasets sho…
Grounding Large Language Models in Interactive Environments with Online Reinforcement Learning
Thomas Carta, Clément Romac, Thomas Wolf +3
Recent works successfully leveraged Large Language Models' (LLM) abilities to capture abstract knowledge about world's physics to solve decision-making problems. Yet, the alignment…
Stein Variational Goal Generation for adaptive Exploration in Multi-Goal Reinforcement Learning
Nicolas Castanet, Sylvain Lamprier, Olivier Sigaud
In multi-goal Reinforcement Learning, an agent can share experience between related training tasks, resulting in better generalization for new tasks at test time. However, when the…
Generative Cooperative Networks for Natural Language Generation
Sylvain Lamprier, Thomas Scialom, Antoine Chaffin +4
Generative Adversarial Networks (GANs) have known a tremendous success for many continuous generation tasks, especially in the field of image generation. However, for discrete outp…
Fairness without the sensitive attribute via Causal Variational Autoencoder
Vincent Grari, Sylvain Lamprier, Marcin Detyniecki
In recent years, most fairness strategies in machine learning models focus on mitigating unwanted biases by assuming that the sensitive information is observed. However this is not…
A Neural Tangent Kernel Perspective of GANs
Jean-Yves Franceschi, Emmanuel de Bézenac, Ibrahim Ayed +3
We propose a novel theoretical framework of analysis for Generative Adversarial Networks (GANs). We reveal a fundamental flaw of previous analyses which, by incorrectly modeling GA…
Improving Robustness of Deep Reinforcement Learning Agents: Environment Attack based on the Critic Network
Lucas Schott, Hatem Hajri, Sylvain Lamprier
To improve policy robustness of deep reinforcement learning agents, a line of recent works focus on producing disturbances of the environment. Existing approaches of the literature…
Stochastic sparse adversarial attacks
Manon Césaire, Lucas Schott, Hatem Hajri +2
This paper introduces stochastic sparse adversarial attacks (SSAA), standing as simple, fast and purely noise-based targeted and untargeted attacks of neural network classifiers (N…
Learning Unbiased Representations via Rényi Minimization
Vincent Grari, Oualid El Hajouji, Sylvain Lamprier +1
In recent years, significant work has been done to include fairness constraints in the training objective of machine learning algorithms. Many state-of the-art algorithms tackle th…
Adversarial Learning for Counterfactual Fairness
Vincent Grari, Sylvain Lamprier, Marcin Detyniecki
In recent years, fairness has become an important topic in the machine learning research community. In particular, counterfactual fairness aims at building prediction models which…
PDE-Driven Spatiotemporal Disentanglement
Jérémie Donà, Jean-Yves Franceschi, Sylvain Lamprier +1
A recent line of work in the machine learning community addresses the problem of predicting high-dimensional spatiotemporal phenomena by leveraging specific tools from the differen…
Fair Adversarial Gradient Tree Boosting
Vincent Grari, Boris Ruf, Sylvain Lamprier +1
Fair classification has become an important topic in machine learning research. While most bias mitigation strategies focus on neural networks, we noticed a lack of work on fair cl…
Fairness-Aware Neural Réyni Minimization for Continuous Features
Vincent Grari, Boris Ruf, Sylvain Lamprier +1
The past few years have seen a dramatic rise of academic and societal interest in fair machine learning. While plenty of fair algorithms have been proposed recently to tackle this…
Learning Information Spread in Content Networks
Cédric Lagnier, Simon Bourigault, Sylvain Lamprier +2
We introduce a model for predicting the diffusion of content information on social media. When propagation is usually modeled on discrete graph structures, we introduce here a cont…