298 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Université de LilleFR364 papers
- Centre National de la Recherche ScientifiqueFR257 papers
- École Centrale de LilleFR212 papers
- Centre de Recherche en InformatiqueFR203 papers
- Université Paris-SaclayFR74 papers
- Institut d'Astrophysique SpatialeFR64 papers
- Sorbonne UniversitéFR59 papers
- Institut de Recherche en Astrophysique et PlanétologieFR58 papers
- Université Paris CitéFR57 papers
- Université Paris Sciences et LettresFR53 papers
- Ludwig-Maximilians-Universität MünchenDE52 papers
- CEA Paris-SaclayFR51 papers
7 papers · 2 filters
How Hard is it to Confuse a World Model?
Waris Radji, Odalric-Ambrym Maillard
In reinforcement learning (RL) theory, the concept of most confusing instances is central to establishing regret lower bounds, that is, the minimal exploration needed to solve a pr…
Dimension Agnostic Testing of Survey Data Credibility through the Lens of Regression
Debabrota Basu, Sourav Chakraborty, Debarshi Chanda +3
Assessing whether a sample survey credibly represents the population is a critical question for ensuring the validity of downstream research. Generally, this problem reduces to est…
Quenched large deviations for Monte Carlo integration with Coulomb gases
Martin Rouault, Rémi Bardenet, Mylène Maïda
Gibbs measures, such as Coulomb gases, are popular in modelling systems of interacting particles. Recently, we proposed to use Gibbs measures as randomized numerical integration al…
Search-Optimized Quantization in Biomedical Ontology Alignment
Oussama Bouaggad, Natalia Grabar
In the fast-moving world of AI, as organizations and researchers develop more advanced models, they face challenges due to their sheer size and computational demands. Deploying suc…
Fedivertex: a Graph Dataset based on Decentralized Social Networks for Trustworthy Machine Learning
Marc Damie, Edwige Cyffers
Decentralized machine learning - where each client keeps its own data locally and uses its own computational resources to collaboratively train a model by exchanging peer-to-peer m…
TAMIS: Tailored Membership Inference Attacks on Synthetic Data
Paul Andrey, Batiste Le Bars, Marc Tommasi
Membership Inference Attacks (MIA) enable to empirically assess the privacy of a machine learning algorithm. In this paper, we propose TAMIS, a novel MIA against differentially-pri…
Sublinear Algorithms for Wasserstein and Total Variation Distances: Applications to Fairness and Privacy Auditing
Debabrota Basu, Debarshi Chanda
Resource-efficiently computing representations of probability distributions and the distances between them while only having access to the samples is a fundamental and useful probl…