b'Hoi-To Wai'

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

This paper proposes a new algorithm – the Momentum-assisted Single-times...
02/15/2021 ∙ by Prashant Khanduri, et al. ∙ 0 ∙ share

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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...
01/30/2021 ∙ by Alain Durmus, et al. ∙ 0 ∙ share

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Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models

In federated learning, models are learned from users' data that are held...
12/27/2020 ∙ by Ruiyuan Wu, et al. ∙ 0 ∙ share

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A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm

The Expectation Maximization (EM) algorithm is of key importance for inf...
11/30/2020 ∙ by Gersende Fort, et al. ∙ 0 ∙ share

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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...
11/24/2020 ∙ by Gersende Fort, et al. ∙ 0 ∙ share

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

This paper presents a finite time convergence analysis for a decentraliz...
08/18/2020 ∙ by Hoi-To Wai, et al. ∙ 0 ∙ share

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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 ...
08/04/2020 ∙ by Raksha Ramakrishna, et al. ∙ 0 ∙ share

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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 ...
07/10/2020 ∙ by Mingyi Hong, 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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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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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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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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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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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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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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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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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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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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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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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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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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Hoi-To Wai

Featured Co-authors

Zhaoran Wang

Zhuoran Yang

Ali Jadbabaie

Mingyi Hong

Eric Moulines

Xiao Fu

Alain Durmus

Santiago Segarra

Angelia Nedić

Julie Josse

César A. Uribe

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

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

Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models

A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm

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

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

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

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

Convergence Analysis of Riemannian Stochastic Approximation Schemes

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

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

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

Spectral partitioning of time-varying networks with unobserved edges

Non-asymptotic Analysis of Biased Stochastic Approximation Scheme

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

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

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

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

On Curvature-aided Incremental Aggregated Gradient Methods

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

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