978 citations
- École Normale Supérieure - PSLFR306 papers
- Centre National de la Recherche ScientifiqueFR135 papers
- Institut national de recherche en sciences et technologies du numériqueFR94 papers
- Université Paris Sciences et LettresFR89 papers
- Université Paris CitéFR23 papers
- Sorbonne UniversitéFR19 papers
- SIERRA: Apprentissage Statistique et optimisationFR17 papers
- Université de MontréalCA15 papers
- École Polytechnique Fédérale de LausanneCH14 papers
- Czech Technical University in PragueCZ13 papers
- École PolytechniqueFR13 papers
- University of EdinburghGB13 papers
41 papers · 1 filter
Effective Energy, Interactions And Out Of Equilibrium Nature Of Scalar Active Matter
Antonin Brossollet, Etienne Lempereur, Stéphane Mallat +1
Estimating the effective energy, of a stationary probability distribution is a challenge for non-equilibrium steady states. Its solution could offer a novel framewor…
Bow Metrics and Hyperbolicity
Feodor F. Dragan, Guillaume Ducoffe, Michel Habib +1
A ()-bow metric was defined in (Dragan & Ducoffe, 2023) as a far reaching generalization of an -metric (which is equivalent to a ()-bow metric). A graph is…
Optimal Classification under Performative Distribution Shift
Edwige Cyffers, Muni Sreenivas Pydi, Jamal Atif +1
Performative learning addresses the increasingly pervasive situations in which algorithmic decisions may induce changes in the data distribution as a consequence of their public de…
Low bit-flip rate probabilistic error cancellation
Mathys Rennela, Harold Ollivier
Noise remains one of the most significant challenges in the development of reliable and scalable quantum processors. While quantum error correction and mitigation techniques offer…
When Quantum and Classical Models Disagree: Learning Beyond Minimum Norm Least Square
Slimane Thabet, Léo Monbroussou, Eliott Z. Mamon +1
Quantum Machine Learning algorithms based on Variational Quantum Circuits (VQCs) are important candidates for useful application of quantum computing. It is known that a VQC is a l…
Learning Controlled Stochastic Differential Equations
Luc Brogat-Motte, Riccardo Bonalli, Alessandro Rudi
We study the problem of learning controlled stochastic differential equations (SDEs) \[ dX_t = b(t,X_t,u_t)\,dt + σ(t,X_t,u_t)\,dW_t, \] whose drift and diffusion depend nonlinearl…