678 citations
- Centre National de la Recherche ScientifiqueFR250 papers
- Laboratoire de Probabilités, Statistique et ModélisationFR238 papers
- Sorbonne UniversitéFR171 papers
- Université Paris CitéFR142 papers
- Université Pierre-et-Marie-CurieFR116 papers
- Université Paris DiderotFR83 papers
- Psychiatric Medicine AssociatesUS67 papers
- École Normale Supérieure - PSLFR63 papers
- Département de mathématiques et applicationsFR55 papers
- Sorbonne Paris CitéFR42 papers
- Centre de Recherche en Économie et StatistiqueFR35 papers
- Institut Universitaire de FranceFR34 papers
1,101 papers
What is a Fluctuation Theorem?
Noé Cuneo, Vojkan Jakšić, Claude-Alain Pillet +1
This book provides a modern review of Fluctuation Relations and Fluctuation Theorems in nonequilibrium statistical mechanics. It focuses on the pioneering perspectives of Gallavott…
Polyak-Łojasiewicz inequality is essentially no more general than strong convexity for functions
Aziz Ben Nejma
The Polyak-Łojasiewicz (PŁ) inequality extends the favorable optimization properties of strongly convex functions to a broader class of functions. In this paper, we prove a theorem…
Cross-Domain Offshore Wind Power Forecasting: Transfer Learning Through Meteorological Clusters
Dominic Weisser, Chloé Hashimoto-Cullen, Benjamin Guedj
Ambitious decarbonisation targets are rapidly increasing the commission of new offshore wind farms. For these newly commissioned plants to run, accurate power forecasts are needed…
Quantitative rigidity of the Wasserstein contraction under convolution
Max Fathi, Michael Goldman, Daniel Tsodyks
The aim of this paper is to investigate the contraction properties of -Wasserstein distances with respect to convolution in Euclidean spaces both qualitatively and quantitativel…
Fairness Meets Privacy: Integrating Differential Privacy and Demographic Parity in Multi-class Classification
Lilian Say, Christophe Denis, Rafael Pinot
The increasing use of machine learning in sensitive applications demands algorithms that simultaneously preserve data privacy and ensure fairness across potentially sensitive sub-p…
Maxitive Donsker-Varadhan Formulation for Possibilistic Variational Inference
Jasraj Singh, Shelvia Wongso, Jeremie Houssineau +1
Variational inference (VI) is a cornerstone of modern Bayesian learning, enabling approximate inference in complex models. However, its formulation depends on expectations and dive…