b'Hoi-To Wai'

Robust Distributed Optimization With Randomly Corrupted Gradients

In this paper, we propose a first-order distributed optimization algorit...
06/28/2021 ∙ by Berkay Turan, et al. ∙ 0 ∙ share

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Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

This paper provides a non-asymptotic analysis of linear stochastic appro...
06/02/2021 ∙ by Alain Durmus, et al. ∙ 0 ∙ share

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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

Julie Josse

Angelia Nedić

Songtao Lu

Robust Distributed Optimization With Randomly Corrupted Gradients

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

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