Search Results for author: Éric Moulines

Found 7 papers, 1 papers with code

On Riemannian Stochastic Approximation Schemes with Fixed Step-Size

no code implementations15 Feb 2021 Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said

This result gives rise to a family of stationary distributions indexed by the step-size, which is further shown to converge to a Dirac measure, concentrated at the solution of the problem at hand, as the step-size goes to 0.

Convergence Analysis of Riemannian Stochastic Approximation Schemes

no code implementations27 May 2020 Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said, Hoi-To Wai

This paper analyzes the convergence for a large class of Riemannian stochastic approximation (SA) schemes, which aim at tackling stochastic optimization problems.

Stochastic Optimization

Unifying mirror descent and dual averaging

no code implementations30 Oct 2019 Anatoli Juditsky, Joon Kwon, Éric Moulines

We introduce and analyze a new family of first-order optimization algorithms which generalizes and unifies both mirror descent and dual averaging.

On stochastic gradient Langevin dynamics with dependent data streams: the fully non-convex case

no code implementations30 May 2019 Ngoc Huy Chau, Éric Moulines, Miklos Rásonyi, Sotirios Sabanis, Ying Zhang

We consider the problem of sampling from a target distribution, which is \emph {not necessarily logconcave}, in the context of empirical risk minimization and stochastic optimization as presented in Raginsky et al. (2017).

Stochastic Optimization

Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames

no code implementations NeurIPS 2018 Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Éric Moulines

In this paper, we introduce a low-rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously.


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