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no code implementations • 1 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.

no code implementations • 14 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.

no code implementations • 9 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.

no code implementations • 4 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.

no code implementations • 28 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.

no code implementations • 28 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.

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 $\{({\bf A}_n, {\bf b}_n): n \in \mathbb{N}^*\}$.

no code implementations • NeurIPS 2021 • Prashant Khanduri, Siliang Zeng, Mingyi Hong, Hoi-To Wai, Zhaoran Wang, Zhuoran Yang

We focus on bilevel problems where the lower level subproblem is strongly-convex and the upper level objective function is smooth.

no code implementations • 30 Jan 2021 • Alain Durmus, Eric Moulines, Alexey Naumov, Sergey Samsonov, Hoi-To Wai

This paper studies the exponential stability of random matrix products driven by a general (possibly unbounded) state space Markov chain.

no code implementations • 20 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.

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.

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).

no code implementations • 30 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.

no code implementations • 24 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.

no code implementations • 18 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.

no code implementations • 4 Aug 2020 • Raksha Ramakrishna, Hoi-To Wai, Anna Scaglione

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

no code implementations • 10 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.

no code implementations • 27 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.

no code implementations • 4 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.

no code implementations • 14 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).

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.

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.

no code implementations • 26 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.

no code implementations • 2 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.

no code implementations • 16 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.

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.

no code implementations • 5 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.

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.

1 code implementation • 31 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.

no code implementations • 22 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.

no code implementations • 24 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.

no code implementations • 20 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.

no code implementations • 5 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.

no code implementations • 21 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.

no code implementations • 5 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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