11 citations · 18 across the 6 of their papers we have counts for
6 papers
OneBatchPAM: A Fast and Frugal K-Medoids Algorithm
Antoine de Mathelin, Nicolas Enrique Cecchi, François Deheeger +2
This paper proposes a novel k-medoids approximation algorithm to handle large-scale datasets with reasonable computational time and memory complexity. We develop a local-search alg…
SKADA-Bench: Benchmarking Unsupervised Domain Adaptation Methods with Realistic Validation On Diverse Modalities
Yanis Lalou, Théo Gnassounou, Antoine Collas +6
Unsupervised Domain Adaptation (DA) consists of adapting a model trained on a labeled source domain to perform well on an unlabeled target domain with some data distribution shift.…
Deep Out-of-Distribution Uncertainty Quantification via Weight Entropy Maximization
Antoine de Mathelin, François Deheeger, Mathilde Mougeot +1
This paper deals with uncertainty quantification and out-of-distribution detection in deep learning using Bayesian and ensemble methods. It proposes a practical solution to the lac…
Deep Anti-Regularized Ensembles provide reliable out-of-distribution uncertainty quantification
Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot +1
We consider the problem of uncertainty quantification in high dimensional regression and classification for which deep ensemble have proven to be promising methods. Recent observat…
A Binded VAE for Inorganic Material Generation
Fouad Oubari, Antoine de Mathelin, Rodrigue Décatoire +1
Designing new industrial materials with desired properties can be very expensive and time consuming. The main difficulty is to generate compounds that correspond to realistic mater…
Discrepancy-Based Active Learning for Domain Adaptation
Antoine de Mathelin, Francois Deheeger, Mathilde Mougeot +1
The goal of the paper is to design active learning strategies which lead to domain adaptation under an assumption of Lipschitz functions. Building on previous work by Mansour et al…