b'Hoi-To Wai'

research

∙ 05/11/2023

Clustering of Time-Varying Graphs Based on Temporal Label Smoothness

We propose a node clustering method for time-varying graphs based on the...

0 Katsuki Fukumoto, et al. ∙

research

∙ 02/22/2023

Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning

Stochastic approximation (SA) is a classical algorithm that has had sinc...

0 Aymeric Dieuleveut, et al. ∙

research

∙ 07/28/2022

Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation

This paper considers a joint multi-graph inference and clustering proble...

0 Yiran He, et al. ∙

research

∙ 02/01/2022

DoCoM-SGT: Doubly Compressed Momentum-assisted Stochastic Gradient Tracking Algorithm for Communication Efficient Decentralized Learning

This paper proposes the Doubly Compressed Momentum-assisted Stochastic G...

0 Chung-Yiu Yau, et al. ∙

research

∙ 10/14/2021

On the Stability of Low Pass Graph Filter With a Large Number of Edge Rewires

Recently, the stability of graph filters has been studied as one of the ...

0 Hoang-Son Nguyen, et al. ∙

research

∙ 10/09/2021

An Empirical Study on Compressed Decentralized Stochastic Gradient Algorithms with Overparameterized Models

This paper considers decentralized optimization with application to mach...

0 Arjun Ashok Rao, et al. ∙

research

∙ 10/04/2021

Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima

To regulate a social system comprised of self-interested agents, economi...

0 Boyi Liu, et al. ∙

research

∙ 06/28/2021

Robust Distributed Optimization With Randomly Corrupted Gradients

In this paper, we propose a first-order distributed optimization algorit...

0 Berkay Turan, et al. ∙

research

∙ 06/02/2021

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

This paper provides a non-asymptotic analysis of linear stochastic appro...

0 Alain Durmus, et al. ∙

research

∙ 02/15/2021

A Momentum-Assisted Single-Timescale Stochastic Approximation Algorithm for Bilevel Optimization

This paper proposes a new algorithm – the Momentum-assisted Single-times...

0 Prashant Khanduri, et al. ∙

research

∙ 01/30/2021

On the Stability of Random Matrix Product with Markovian Noise: Application to Linear Stochastic Approximation and TD Learning

This paper studies the exponential stability of random matrix products d...

0 Alain Durmus, et al. ∙

research

∙ 12/27/2020

Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models

In federated learning, models are learned from users' data that are held...

0 Ruiyuan Wu, et al. ∙

research

∙ 11/30/2020

A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm

The Expectation Maximization (EM) algorithm is of key importance for inf...

0 Gersende Fort, et al. ∙

research

∙ 11/24/2020

Geom-SPIDER-EM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex Finite-Sum Optimization

The Expectation Maximization (EM) algorithm is a key reference for infer...

0 Gersende Fort, et al. ∙

research

∙ 08/18/2020

On the Convergence of Consensus Algorithms with Markovian Noise and Gradient Bias

This paper presents a finite time convergence analysis for a decentraliz...

0 Hoi-To Wai, et al. ∙

research

∙ 08/04/2020

A User Guide to Low-Pass Graph Signal Processing and its Applications

The notion of graph filters can be used to define generative models for ...

0 Raksha Ramakrishna, et al. ∙

research

∙ 07/10/2020

A Two-Timescale Framework for Bilevel Optimization: Complexity Analysis and Application to Actor-Critic

This paper analyzes a two-timescale stochastic algorithm for a class of ...

0 Mingyi Hong, et al. ∙

research

∙ 05/27/2020

Convergence Analysis of Riemannian Stochastic Approximation Schemes

This paper analyzes the convergence for a large class of Riemannian stoc...

0 Alain Durmus, et al. ∙

research

∙ 02/04/2020

Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise

Linear two-timescale stochastic approximation (SA) scheme is an importan...

0 Maxim Kaledin, et al. ∙

research

∙ 01/14/2020

Distributed Learning in the Non-Convex World: From Batch to Streaming Data, and Beyond

Distributed learning has become a critical enabler of the massively conn...

0 Tsung-Hui Chang, et al. ∙

research

∙ 10/28/2019

On the Global Convergence of (Fast) Incremental Expectation Maximization Methods

The EM algorithm is one of the most popular algorithm for inference in l...

0 Belhal Karimi, et al. ∙

research

∙ 04/26/2019

Spectral partitioning of time-varying networks with unobserved edges

We discuss a variant of `blind' community detection, in which we aim to ...

0 Michael T. Schaub, et al. ∙

research

∙ 02/02/2019

Non-asymptotic Analysis of Biased Stochastic Approximation Scheme

Stochastic approximation (SA) is a key method used in statistical learni...

0 Belhal Karimi, et al. ∙

research

∙ 01/16/2019

Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization

This work considers the problem of computing the canonical polyadic deco...

0 Xiao Fu, et al. ∙

research

∙ 12/20/2018

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

Many applications of machine learning involve the analysis of large data...

0 Geneviève Robin, et al. ∙

research

∙ 09/05/2018

Blind Community Detection from Low-rank Excitations of a Graph Filter

This paper considers a novel framework to detect communities in a graph ...

0 Hoi-To Wai, et al. ∙

research

∙ 06/03/2018

Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization

Despite the success of single-agent reinforcement learning, multi-agent ...

0 Hoi-To Wai, et al. ∙

research

∙ 05/31/2018

On Curvature-aided Incremental Aggregated Gradient Methods

This paper studies an acceleration technique for incremental aggregated ...

0 Hoi-To Wai, et al. ∙

research

∙ 03/22/2018

SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization

We propose and analyze a new stochastic gradient method, which we call S...

0 Hoi-To Wai, et al. ∙

research

∙ 10/24/2017

Curvature-aided Incremental Aggregated Gradient Method

We propose a new algorithm for finite sum optimization which we call the...

0 Hoi-To Wai, et al. ∙

research

∙ 12/20/2016

RIDS: Robust Identification of Sparse Gene Regulatory Networks from Perturbation Experiments

Reconstructing the causal network in a complex dynamical system plays a ...

0 Hoi-To Wai, et al. ∙

research

∙ 01/21/2016

Active Sensing of Social Networks

This paper develops an active sensing method to estimate the relative we...

0 Hoi-To Wai, et al. ∙

research

∙ 10/05/2015

On the Online Frank-Wolfe Algorithms for Convex and Non-convex Optimizations

In this paper, the online variants of the classical Frank-Wolfe algorith...

0 Jean Lafond, et al. ∙

Hoi-To Wai

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