Search Results for author: Hoi-To Wai

Found 35 papers, 1 papers with code

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

no code implementations1 Feb 2022 Chung-Yiu Yau, Hoi-To Wai

This paper proposes the Doubly Compressed Momentum-assisted Stochastic Gradient Tracking algorithm (DoCoM-SGT) for communication efficient decentralized learning.

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

no code implementations14 Oct 2021 Hoang-Son Nguyen, Yiran He, Hoi-To Wai

The stability of a graph filter characterizes the effect of topology perturbation on the output of a graph filter, a fundamental building block for GCNs.

Stochastic Block Model

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

no code implementations9 Oct 2021 Arjun Ashok Rao, Hoi-To Wai

In this work, we present an empirical analysis on the performance of compressed decentralized stochastic gradient (DSG) algorithms with overparameterized NNs.

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

no code implementations4 Oct 2021 Boyi Liu, Jiayang Li, Zhuoran Yang, Hoi-To Wai, Mingyi Hong, Yu Marco Nie, Zhaoran Wang

To regulate a social system comprised of self-interested agents, economic incentives (e. g., taxes, tolls, and subsidies) are often required to induce a desirable outcome.

Detecting Central Nodes from Low-rank Excited Graph Signals via Structured Factor Analysis

no code implementations28 Sep 2021 Yiran He, Hoi-To Wai

For general low pass graph filter, we show that the graph signals can be described by a structured factor model featuring the product between a low-rank plus sparse factor and an unstructured factor.

Robust Distributed Optimization With Randomly Corrupted Gradients

no code implementations28 Jun 2021 Berkay Turan, Cesar A. Uribe, Hoi-To Wai, Mahnoosh Alizadeh

In this paper, we propose a first-order distributed optimization algorithm that is provably robust to Byzantine failures-arbitrary and potentially adversarial behavior, where all the participating agents are prone to failure.

Distributed Optimization

Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize

no code implementations NeurIPS 2021 Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Kevin Scaman, Hoi-To Wai

This family of methods arises in many machine learning tasks and is used to obtain approximate solutions of a linear system $\bar{A}\theta = \bar{b}$ for which $\bar{A}$ and $\bar{b}$ can only be accessed through random estimates $\{({\bf A}_n, {\bf b}_n): n \in \mathbb{N}^*\}$.

Identifying First-order Lowpass Graph Signals using Perron Frobenius Theorem

no code implementations20 Jan 2021 Yiran He, Hoi-To Wai

Utilizing this observation, we develop a simple detector that answers if a given data set is produced by a first-order lowpass graph filter.

Denoising Graph Learning

A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm

no code implementations NeurIPS 2020 Gersende Fort, Eric Moulines, Hoi-To Wai

The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations.

Provably Efficient Neural GTD for Off-Policy Learning

no code implementations NeurIPS 2020 Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong

This paper studies a gradient temporal difference (GTD) algorithm using neural network (NN) function approximators to minimize the mean squared Bellman error (MSBE).

A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm

no code implementations30 Nov 2020 Gersende Fort, Eric Moulines, Hoi-To Wai

The Expectation Maximization (EM) algorithm is of key importance for inference in latent variable models including mixture of regressors and experts, missing observations.

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

no code implementations24 Nov 2020 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 learning setting.

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

no code implementations18 Aug 2020 Hoi-To Wai

This rate is comparable to the best known performances of SA in a centralized setting with a non-convex potential function.

Multi-agent Reinforcement Learning reinforcement-learning

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

no code implementations4 Aug 2020 Raksha Ramakrishna, Hoi-To Wai, Anna Scaglione

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

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

no code implementations10 Jul 2020 Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang

Bilevel optimization is a class of problems which exhibit a two-level structure, and its goal is to minimize an outer objective function with variables which are constrained to be the optimal solution to an (inner) optimization problem.

Bilevel Optimization Hyperparameter Optimization

Convergence Analysis of Riemannian Stochastic Approximation Schemes

no code implementations27 May 2020 Alain Durmus, Pablo Jiménez, Éric Moulines, Salem Said, Hoi-To Wai

This paper analyzes the convergence for a large class of Riemannian stochastic approximation (SA) schemes, which aim at tackling stochastic optimization problems.

Stochastic Optimization

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

no code implementations4 Feb 2020 Maxim Kaledin, Eric Moulines, Alexey Naumov, Vladislav Tadic, Hoi-To Wai

Our bounds show that there is no discrepancy in the convergence rate between Markovian and martingale noise, only the constants are affected by the mixing time of the Markov chain.

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

no code implementations14 Jan 2020 Tsung-Hui Chang, Mingyi Hong, Hoi-To Wai, Xinwei Zhang, Songtao Lu

In particular, we {provide a selective review} about the recent techniques developed for optimizing non-convex models (i. e., problem classes), processing batch and streaming data (i. e., data types), over the networks in a distributed manner (i. e., communication and computation paradigm).

Variance Reduced Policy Evaluation with Smooth Function Approximation

no code implementations NeurIPS 2019 Hoi-To Wai, Mingyi Hong, Zhuoran Yang, Zhaoran Wang, Kexin Tang

Policy evaluation with smooth and nonlinear function approximation has shown great potential for reinforcement learning.

reinforcement-learning

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

no code implementations NeurIPS 2019 Belhal Karimi, Hoi-To Wai, Eric Moulines, Marc Lavielle

To alleviate this problem, Neal and Hinton have proposed an incremental version of the EM (iEM) in which at each iteration the conditional expectation of the latent data (E-step) is updated only for a mini-batch of observations.

Spectral partitioning of time-varying networks with unobserved edges

no code implementations26 Apr 2019 Michael T. Schaub, Santiago Segarra, Hoi-To Wai

We discuss a variant of `blind' community detection, in which we aim to partition an unobserved network from the observation of a (dynamical) graph signal defined on the network.

Community Detection

Non-asymptotic Analysis of Biased Stochastic Approximation Scheme

no code implementations2 Feb 2019 Belhal Karimi, Blazej Miasojedow, Eric Moulines, Hoi-To Wai

We illustrate these settings with the online EM algorithm and the policy-gradient method for average reward maximization in reinforcement learning.

reinforcement-learning

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

no code implementations16 Jan 2019 Xiao Fu, Shahana Ibrahim, Hoi-To Wai, Cheng Gao, Kejun Huang

In this work, we propose a stochastic optimization framework for large-scale CPD with constraints/regularizations.

Stochastic Optimization

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

no code implementations NeurIPS 2018 Geneviève Robin, Hoi-To Wai, Julie Josse, Olga Klopp, Éric Moulines

In this paper, we introduce a low-rank interaction and sparse additive effects (LORIS) model which combines matrix regression on a dictionary and low-rank design, to estimate main effects and interactions simultaneously.

Imputation

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

no code implementations5 Sep 2018 Hoi-To Wai, Santiago Segarra, Asuman E. Ozdaglar, Anna Scaglione, Ali Jadbabaie

The paper shows that communities can be detected by applying a spectral method to the covariance matrix of graph signals.

Community Detection

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

no code implementations NeurIPS 2018 Hoi-To Wai, Zhuoran Yang, Zhaoran Wang, Mingyi Hong

Despite the success of single-agent reinforcement learning, multi-agent reinforcement learning (MARL) remains challenging due to complex interactions between agents.

Multi-agent Reinforcement Learning reinforcement-learning

Accelerating Incremental Gradient Optimization with Curvature Information

1 code implementation31 May 2018 Hoi-To Wai, Wei Shi, Cesar A. Uribe, Angelia Nedich, Anna Scaglione

This paper studies an acceleration technique for incremental aggregated gradient ({\sf IAG}) method through the use of \emph{curvature} information for solving strongly convex finite sum optimization problems.

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

no code implementations22 Mar 2018 Hoi-To Wai, Nikolaos M. Freris, Angelia Nedic, Anna Scaglione

We propose and analyze a new stochastic gradient method, which we call Stochastic Unbiased Curvature-aided Gradient (SUCAG), for finite sum optimization problems.

Distributed Optimization

Curvature-aided Incremental Aggregated Gradient Method

no code implementations24 Oct 2017 Hoi-To Wai, Wei Shi, Angelia Nedic, Anna Scaglione

We propose a new algorithm for finite sum optimization which we call the curvature-aided incremental aggregated gradient (CIAG) method.

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

no code implementations20 Dec 2016 Hoi-To Wai, Anna Scaglione, Uzi Harush, Baruch Barzel, Amir Leshem

To overcome this challenge, we develop the Robust IDentification of Sparse networks (RIDS) method that reconstructs the GRN from a small number of perturbation experiments.

Decentralized Frank-Wolfe Algorithm for Convex and Non-convex Problems

no code implementations5 Dec 2016 Hoi-To Wai, Jean Lafond, Anna Scaglione, Eric Moulines

The convergence of the proposed algorithm is studied by viewing the decentralized algorithm as an inexact FW algorithm.

Matrix Completion Sparse Learning

Active Sensing of Social Networks

no code implementations21 Jan 2016 Hoi-To Wai, Anna Scaglione, Amir Leshem

The model used for the regression is based on the steady state equation in the linear DeGroot model under the influence of stubborn agents, i. e., agents whose opinions are not influenced by their neighbors.

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

no code implementations5 Oct 2015 Jean Lafond, Hoi-To Wai, Eric Moulines

With a strongly convex stochastic cost and when the optimal solution lies in the interior of the constraint set or the constraint set is a polytope, the regret bound and anytime optimality are shown to be ${\cal O}( \log^3 T / T )$ and ${\cal O}( \log^2 T / T)$, respectively, where $T$ is the number of rounds played.

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