76 citations · 215 across the 31 of their papers we have counts for
6 papers · 1 filter
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…
To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs
Thomas Scialom, Paul-Alexis Dray, Sylvain Lamprier +2
Due to the discrete nature of words, language GANs require to be optimized from rewards provided by discriminator networks, via reinforcement learning methods. This is a much harde…
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…
QuestEval: Summarization Asks for Fact-based Evaluation
Thomas Scialom, Paul-Alexis Dray, Patrick Gallinari +4
Summarization evaluation remains an open research problem: current metrics such as ROUGE are known to be limited and to correlate poorly with human judgments. To alleviate this iss…
Data-QuestEval: A Referenceless Metric for Data-to-Text Semantic Evaluation
Clément Rebuffel, Thomas Scialom, Laure Soulier +5
QuestEval is a reference-less metric used in text-to-text tasks, that compares the generated summaries directly to the source text, by automatically asking and answering questions.…
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…