183 citations · 726 across the 47 of their papers we have counts for
4 papers · 1 filter
First Order Methods with Markovian Noise: from Acceleration to Variational Inequalities
Aleksandr Beznosikov, Sergey Samsonov, Marina Sheshukova +3
This paper delves into stochastic optimization problems that involve Markovian noise. We present a unified approach for the theoretical analysis of first-order gradient methods for…
Orthogonal Directions Constrained Gradient Method: from non-linear equality constraints to Stiefel manifold
Sholom Schechtman, Daniil Tiapkin, Michael Muehlebach +1
We consider the problem of minimizing a non-convex function over a smooth manifold . We propose a novel algorithm, the Orthogonal Directions Constrained Gradient Metho…
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Aymeric Dieuleveut, Gersende Fort, Eric Moulines +1
Stochastic Approximation (SA) is a classical algorithm that has had since the early days a huge impact on signal processing, and nowadays on machine learning, due to the necessity…
Analysis of nonsmooth stochastic approximation: the differential inclusion approach
Szymon Majewski, Błażej Miasojedow, Eric Moulines
In this paper we address the convergence of stochastic approximation when the functions to be minimized are not convex and nonsmooth. We show that the "mean-limit" approach to the…