Concept
Théorème de convergence monotone
Publications associées (52)
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State-of-the-art methods for self-supervised sequential action alignment rely on deep networks that find correspon- dences across videos in time. They either learn frame-to- frame mapping across sequences, which does not leverage temporal information, or a ...
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We study generalization properties of random features (RF) regression in high dimensions optimized by stochastic gradient descent (SGD) in under-/overparameterized regime. In this work, we derive precise non-asymptotic error bounds of RF regression under b ...
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Mixed-precision algorithms combine low-and high-precision computations in order to benefit from the performance gains of reduced-precision without sacrificing accuracy. In this work, we design mixed-precision Runge-Kutta-Chebyshev (RKC) methods, where high ...
ACADEMIC PRESS INC ELSEVIER SCIENCE2022Decentralized Proximal Gradient Algorithms With Linear Convergence Rates
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This article studies a class of nonsmooth decentralized multiagent optimization problems where the agents aim at minimizing a sum of local strongly-convex smooth components plus a common nonsmooth term. We propose a general primal-dual algorithmic framewor ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2021Analytical and Machine Learning Methods for the Complete Safe Coordination of Astrobot Swarms
The recent generations of massive spectroscopic surveys aim at the ray collection from a multitude of cosmological targets in the course of observations. For this purpose, astrobots are used to change the configuration of optical fibers from one observatio ...
EPFL2021Variance-Reduced Stochastic Learning Under Random Reshuffling
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Several useful variance-reduced stochastic gradient algorithms, such as SVRG, SAGA, Finito, and SAG, have been proposed to minimize empirical risks with linear convergence properties to the exact minimizer. The existing convergence results assume uniform d ...
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020MATHICSE Technical Report : Generalized Parallel Tempering on Bayesian Inverse Problems
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In the current work we present two generalizations of the Parallel Tempering algorithm, inspired by the so-called continuous-time Infinite Swapping algorithm. Such a method, found its origins in the molecular dynamics community, and can be understood as th ...
MATHICSE2020Convergent momentum-space OPE and bootstrap equations in conformal field theory
General principles of quantum field theory imply that there exists an operator product expansion (OPE) for Wightman functions in Minkowski momentum space that converges for arbitrary kinematics. This convergence is guaranteed to hold in the sense of a dist ...
SPRINGER2020A Bayesian numerical homogenization method for elliptic multiscale inverse problems
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A new strategy based on numerical homogenization and Bayesian techniques for solving multiscale inverse problems is introduced. We consider a class of elliptic problems which vary at a microscopic scale, and we aim at recovering the highly oscillatory tens ...
2020A local discontinuous Galerkin gradient discretization method for linear and quasilinear elliptic equations
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A local weighted discontinuous Galerkin gradient discretization method for solving elliptic equations is introduced. The local scheme is based on a coarse grid and successively improves the solution solving a sequence of local elliptic problems in high gra ...
EDP SCIENCES S A2019