4 citations · 8 across the 7 of their papers we have counts for
7 papers
Convergence Guarantees for Federated SARSA with Local Training and Heterogeneous Agents
Paul Mangold, Eloïse Berthier, Eric Moulines
We present a novel theoretical analysis of Federated SARSA (FedSARSA) with linear function approximation and local training. We establish convergence guarantees for FedSARSA in the…
Enhancing Concept Localization in CLIP-based Concept Bottleneck Models
Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier +2
This paper addresses explainable AI (XAI) through the lens of Concept Bottleneck Models (CBMs) that do not require explicit concept annotations, relying instead on concepts extract…
Concept-Based Mechanistic Interpretability Using Structured Knowledge Graphs
Sofiia Chorna, Kateryna Tarelkina, Eloïse Berthier +1
While concept-based interpretability methods have traditionally focused on local explanations of neural network predictions, we propose a novel framework and interactive tool that…
Explainability for Vision Foundation Models: A Survey
Rémi Kazmierczak, Eloïse Berthier, Goran Frehse +1
As artificial intelligence systems become increasingly integrated into daily life, the field of explainability has gained significant attention. This trend is particularly driven b…
Benchmarking XAI Explanations with Human-Aligned Evaluations
Rémi Kazmierczak, Steve Azzolin, Eloïse Berthier +9
We introduce PASTA (Perceptual Assessment System for explanaTion of Artificial Intelligence), a novel human-centric framework for evaluating eXplainable AI (XAI) techniques in comp…
CLIP-QDA: An Explainable Concept Bottleneck Model
Rémi Kazmierczak, Eloïse Berthier, Goran Frehse +1
In this paper, we introduce an explainable algorithm designed from a multi-modal foundation model, that performs fast and explainable image classification. Drawing inspiration from…
On Double Descent in Reinforcement Learning with LSTD and Random Features
David Brellmann, Eloïse Berthier, David Filliat +1
Temporal Difference (TD) algorithms are widely used in Deep Reinforcement Learning (RL). Their performance is heavily influenced by the size of the neural network. While in supervi…