Hoi-To Wai

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Arizona State University

Curvature-aided Incremental Aggregated Gradient Method

We propose a new algorithm for finite sum optimization which we call the...
10/24/2017 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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

Reconstructing the causal network in a complex dynamical system plays a ...
12/20/2016 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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Active Sensing of Social Networks

This paper develops an active sensing method to estimate the relative we...
01/21/2016 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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

In this paper, the online variants of the classical Frank-Wolfe algorith...
10/05/2015 ∙ by Jean Lafond, et al. ∙ 0 ∙ share

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

We propose and analyze a new stochastic gradient method, which we call S...
03/22/2018 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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On Curvature-aided Incremental Aggregated Gradient Methods

This paper studies an acceleration technique for incremental aggregated ...
05/31/2018 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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

Despite the success of single-agent reinforcement learning, multi-agent ...
06/03/2018 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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

This paper considers a novel framework to detect communities in a graph ...
09/05/2018 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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

Many applications of machine learning involve the analysis of large data...
12/20/2018 ∙ by Geneviève Robin, et al. ∙ 0 ∙ share

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

This work considers the problem of computing the canonical polyadic deco...
01/16/2019 ∙ by Xiao Fu, et al. ∙ 0 ∙ share

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Non-asymptotic Analysis of Biased Stochastic Approximation Scheme

Stochastic approximation (SA) is a key method used in statistical learni...
02/02/2019 ∙ by Belhal Karimi, et al. ∙ 0 ∙ share

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Spectral partitioning of time-varying networks with unobserved edges

We discuss a variant of `blind' community detection, in which we aim to ...
04/26/2019 ∙ by Michael T. Schaub, et al. ∙ 0 ∙ share

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

The EM algorithm is one of the most popular algorithm for inference in l...
10/28/2019 ∙ by Belhal Karimi, et al. ∙ 0 ∙ share

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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...
01/14/2020 ∙ by Tsung-Hui Chang, et al. ∙ 0 ∙ share

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

Linear two-timescale stochastic approximation (SA) scheme is an importan...
02/04/2020 ∙ by Maxim Kaledin, et al. ∙ 0 ∙ share

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Convergence Analysis of Riemannian Stochastic Approximation Schemes

This paper analyzes the convergence for a large class of Riemannian stoc...
05/27/2020 ∙ by Alain Durmus, et al. ∙ 0 ∙ share

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Hoi-To Wai

Featured Co-authors

Zhaoran Wang

Zhuoran Yang

Ali Jadbabaie

Mingyi Hong

Eric Moulines

Xiao Fu

Alain Durmus

Angelia Nedić

Julie Josse

Amir Leshem

Wei Shi

Curvature-aided Incremental Aggregated Gradient Method

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

Active Sensing of Social Networks

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

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

On Curvature-aided Incremental Aggregated Gradient Methods

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

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

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

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

Non-asymptotic Analysis of Biased Stochastic Approximation Scheme

Spectral partitioning of time-varying networks with unobserved edges

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

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

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

Convergence Analysis of Riemannian Stochastic Approximation Schemes