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09/10/2023
Linear Speedup of Incremental Aggregated Gradient Methods on Streaming Data
This paper considers a type of incremental aggregated gradient (IAG) met...
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05/11/2023
Clustering of Time-Varying Graphs Based on Temporal Label Smoothness
We propose a node clustering method for time-varying graphs based on the...
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02/22/2023
Stochastic Approximation Beyond Gradient for Signal Processing and Machine Learning
Stochastic approximation (SA) is a classical algorithm that has had sinc...
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07/28/2022
Online Inference for Mixture Model of Streaming Graph Signals with Non-White Excitation
This paper considers a joint multi-graph inference and clustering proble...
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02/01/2022
DoCoM-SGT: Doubly Compressed Momentum-assisted Stochastic Gradient Tracking Algorithm for Communication Efficient Decentralized Learning
This paper proposes the Doubly Compressed Momentum-assisted Stochastic G...
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10/14/2021
On the Stability of Low Pass Graph Filter With a Large Number of Edge Rewires
Recently, the stability of graph filters has been studied as one of the ...
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10/09/2021
An Empirical Study on Compressed Decentralized Stochastic Gradient Algorithms with Overparameterized Models
This paper considers decentralized optimization with application to mach...
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10/04/2021
Inducing Equilibria via Incentives: Simultaneous Design-and-Play Finds Global Optima
To regulate a social system comprised of self-interested agents, economi...
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06/28/2021
Robust Distributed Optimization With Randomly Corrupted Gradients
In this paper, we propose a first-order distributed optimization algorit...
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06/02/2021
Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
This paper provides a non-asymptotic analysis of linear stochastic appro...
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02/15/2021
A Momentum-Assisted Single-Timescale Stochastic Approximation Algorithm for Bilevel Optimization
This paper proposes a new algorithm – the Momentum-assisted Single-times...
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01/30/2021
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...
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12/27/2020
Federated Block Coordinate Descent Scheme for Learning Global and Personalized Models
In federated learning, models are learned from users' data that are held...
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11/30/2020
A Stochastic Path-Integrated Differential EstimatoR Expectation Maximization Algorithm
The Expectation Maximization (EM) algorithm is of key importance for inf...
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11/24/2020
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...
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08/18/2020
On the Convergence of Consensus Algorithms with Markovian Noise and Gradient Bias
This paper presents a finite time convergence analysis for a decentraliz...
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08/04/2020
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 ...
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07/10/2020
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 ...
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05/27/2020
Convergence Analysis of Riemannian Stochastic Approximation Schemes
This paper analyzes the convergence for a large class of Riemannian stoc...
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02/04/2020
Finite Time Analysis of Linear Two-timescale Stochastic Approximation with Markovian Noise
Linear two-timescale stochastic approximation (SA) scheme is an importan...
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01/14/2020
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...
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10/28/2019
On the Global Convergence of (Fast) Incremental Expectation Maximization Methods
The EM algorithm is one of the most popular algorithm for inference in l...
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04/26/2019
Spectral partitioning of time-varying networks with unobserved edges
We discuss a variant of `blind' community detection, in which we aim to ...
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02/02/2019
Non-asymptotic Analysis of Biased Stochastic Approximation Scheme
Stochastic approximation (SA) is a key method used in statistical learni...
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01/16/2019
Block-Randomized Stochastic Proximal Gradient for Low-Rank Tensor Factorization
This work considers the problem of computing the canonical polyadic deco...
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12/20/2018
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Many applications of machine learning involve the analysis of large data...
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09/05/2018
Blind Community Detection from Low-rank Excitations of a Graph Filter
This paper considers a novel framework to detect communities in a graph ...
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06/03/2018
Multi-Agent Reinforcement Learning via Double Averaging Primal-Dual Optimization
Despite the success of single-agent reinforcement learning, multi-agent ...
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05/31/2018
On Curvature-aided Incremental Aggregated Gradient Methods
This paper studies an acceleration technique for incremental aggregated ...
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03/22/2018
SUCAG: Stochastic Unbiased Curvature-aided Gradient Method for Distributed Optimization
We propose and analyze a new stochastic gradient method, which we call S...
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10/24/2017
Curvature-aided Incremental Aggregated Gradient Method
We propose a new algorithm for finite sum optimization which we call the...
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12/20/2016
RIDS: Robust Identification of Sparse Gene Regulatory Networks from Perturbation Experiments
Reconstructing the causal network in a complex dynamical system plays a ...
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01/21/2016
Active Sensing of Social Networks
This paper develops an active sensing method to estimate the relative we...
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10/05/2015