52 citations · 171 across the 27 of their papers we have counts for
4 papers · 1 filter
: a library for Wasserstein distributionally robust machine learning
Florian Vincent, Waïss Azizian, Franck Iutzeler +1
We present skwdro, a Python library for training robust machine learning models. The library is based on distributionally robust optimization using Wasserstein distances, popular i…
Exact Generalization Guarantees for (Regularized) Wasserstein Distributionally Robust Models
Waïss Azizian, Franck Iutzeler, Jérôme Malick
Wasserstein distributionally robust estimators have emerged as powerful models for prediction and decision-making under uncertainty. These estimators provide attractive generalizat…
Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism
Yu-Guan Hsieh, Franck Iutzeler, Jérôme Malick +1
In this paper, we provide a general framework for studying multi-agent online learning problems in the presence of delays and asynchronicities. Specifically, we propose and analyze…
Rank-one partitioning: formalization, illustrative examples, and a new cluster enhancing strategy
Charlotte Laclau, Franck Iutzeler, Ievgen Redko
In this paper, we introduce and formalize a rank-one partitioning learning paradigm that unifies partitioning methods that proceed by summarizing a data set using a single vector t…