257 citations · 1k across the 75 of their papers we have counts for
12 papers · 1 filter
From topology learning to graph generation: A unifying perspective
Xiaowen Dong, Hoi-To Wai, Siheng Chen +2
Learning graph structures from data is a fundamental problem that spans a wide range of signal processing and machine learning tasks. While significant effort has been made to tack…
Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation
Yiran He, Hoi-To Wai
This paper considers a joint multi-graph inference and clustering problem for simultaneous inference of node centrality and association of graph signals with their graphs. We study…
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
Alain Durmus, Eric Moulines, Alexey Naumov +3
This paper provides a non-asymptotic analysis of linear stochastic approximation (LSA) algorithms with fixed stepsize. This family of methods arises in many machine learning tasks…
On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning
Alain Durmus, Eric Moulines, Alexey Naumov +2
This paper studies the exponential stability of random matrix products driven by a general (possibly unbounded) state space Markov chain. It is a cornerstone in the analysis of sto…
Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization
Gersende Fort, Eric Moulines, Hoi-To Wai
The Expectation Maximization (EM) algorithm is a key reference for inference in latent variable models; unfortunately, its computational cost is prohibitive in the large scale lear…
Convergence Analysis of Riemannian Stochastic Approximation Schemes
Alain Durmus, Pablo Jiménez, Éric Moulines +2
This paper analyzes the convergence for a large class of Riemannian stochastic approximation (SA) schemes, which aim at tackling stochastic optimization problems. In particular, th…
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
Maxim Kaledin, Eric Moulines, Alexey Naumov +2
Linear two-timescale stochastic approximation (SA) scheme is an important class of algorithms which has become popular in reinforcement learning (RL), particularly for the policy e…
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
Belhal Karimi, Hoi-To Wai, Eric Moulines +1
The EM algorithm is one of the most popular algorithm for inference in latent data models. The original formulation of the EM algorithm does not scale to large data set, because th…
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Belhal Karimi, Blazej Miasojedow, Eric Moulines +1
Stochastic approximation (SA) is a key method used in statistical learning. Recently, its non-asymptotic convergence analysis has been considered in many papers. However, most of t…
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Geneviève Robin, Hoi-To Wai, Julie Josse +2
Many applications of machine learning involve the analysis of large data frames-matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples-with…
Curvature-aided Incremental Aggregated Gradient Method
Hoi-To Wai, Wei Shi, Angelia Nedic +1
We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method. Motivated by the problem of training a class…
On the Online Frank-Wolfe Algorithms for Convex and Non-convex Optimizations
Jean Lafond, Hoi-To Wai, Eric Moulines
In this paper, the online variants of the classical Frank-Wolfe algorithm are considered. We consider minimizing the regret with a stochastic cost. The online algorithms only requi…