23 citations · 49 across the 14 of their papers we have counts for
16 papers
From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes
Rubén Garzón, Pauline Baron, Vincent Grari +3
Large language models (LLM) agents may offer tools to predict human responses to surveys. A common technique for defining these agents uses only demographics, for example country,…
Agentic Adversarial QA for Improving Domain-Specific LLMs
Vincent Grari, Ciprian Tomoiaga, Sylvain Lamprier +2
Large Language Models (LLMs), despite extensive pretraining on broad internet corpora, often struggle to adapt effectively to specialized domains. There is growing interest in fine…
Energy Scaling Laws for Diffusion Models: Quantifying Compute in Image Generation
Aniketh Iyengar, Jiaqi Han, Boris Ruf +3
The rapidly growing computational demands of diffusion models for image generation have raised significant concerns about energy consumption and environmental impact. While existin…
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…
SAKE: Steering Activations for Knowledge Editing
Marco Scialanga, Thibault Laugel, Vincent Grari +1
As Large Langue Models have been shown to memorize real-world facts, the need to update this knowledge in a controlled and efficient manner arises. Designed with these constraints…
Controlled Model Debiasing through Minimal and Interpretable Updates
Federico Di Gennaro, Thibault Laugel, Vincent Grari +1
Traditional approaches to learning fair machine learning models often require rebuilding models from scratch, typically without considering potentially existing models. In a contex…
Post-processing fairness with minimal changes
Federico Di Gennaro, Thibault Laugel, Vincent Grari +2
In this paper, we introduce a novel post-processing algorithm that is both model-agnostic and does not require the sensitive attribute at test time. In addition, our algorithm is e…
OptiGrad: A Fair and more Efficient Price Elasticity Optimization via a Gradient Based Learning
Vincent Grari, Marcin Detyniecki
This paper presents a novel approach to optimizing profit margins in non-life insurance markets through a gradient descent-based method, targeting three key objectives: 1) maximizi…
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,…
When mitigating bias is unfair: multiplicity and arbitrariness in algorithmic group fairness
Natasa Krco, Thibault Laugel, Vincent Grari +2
Most research on fair machine learning has prioritized optimizing criteria such as Demographic Parity and Equalized Odds. Despite these efforts, there remains a limited understandi…
A Fair Pricing Model via Adversarial Learning
Vincent Grari, Arthur Charpentier, Marcin Detyniecki
At the core of insurance business lies classification between risky and non-risky insureds, actuarial fairness meaning that risky insureds should contribute more and pay a higher p…
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…
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…
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…