
Robust Distributed Optimization With Randomly Corrupted Gradients
In this paper, we propose a firstorder distributed optimization algorit...
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Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize
This paper provides a nonasymptotic analysis of linear stochastic appro...
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A MomentumAssisted SingleTimescale Stochastic Approximation Algorithm for Bilevel Optimization
This paper proposes a new algorithm – the Momentumassisted Singletimes...
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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...
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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...
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A Stochastic PathIntegrated Differential EstimatoR Expectation Maximization Algorithm
The Expectation Maximization (EM) algorithm is of key importance for inf...
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GeomSPIDEREM: Faster Variance Reduced Stochastic Expectation Maximization for Nonconvex FiniteSum Optimization
The Expectation Maximization (EM) algorithm is a key reference for infer...
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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...
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A User Guide to LowPass Graph Signal Processing and its Applications
The notion of graph filters can be used to define generative models for ...
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A TwoTimescale Framework for Bilevel Optimization: Complexity Analysis and Application to ActorCritic
This paper analyzes a twotimescale stochastic algorithm for a class of ...
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Convergence Analysis of Riemannian Stochastic Approximation Schemes
This paper analyzes the convergence for a large class of Riemannian stoc...
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Finite Time Analysis of Linear Twotimescale Stochastic Approximation with Markovian Noise
Linear twotimescale stochastic approximation (SA) scheme is an importan...
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Distributed Learning in the NonConvex World: From Batch to Streaming Data, and Beyond
Distributed learning has become a critical enabler of the massively conn...
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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...
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Spectral partitioning of timevarying networks with unobserved edges
We discuss a variant of `blind' community detection, in which we aim to ...
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Nonasymptotic Analysis of Biased Stochastic Approximation Scheme
Stochastic approximation (SA) is a key method used in statistical learni...
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BlockRandomized Stochastic Proximal Gradient for LowRank Tensor Factorization
This work considers the problem of computing the canonical polyadic deco...
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Lowrank 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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Blind Community Detection from Lowrank Excitations of a Graph Filter
This paper considers a novel framework to detect communities in a graph ...
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MultiAgent Reinforcement Learning via Double Averaging PrimalDual Optimization
Despite the success of singleagent reinforcement learning, multiagent ...
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On Curvatureaided Incremental Aggregated Gradient Methods
This paper studies an acceleration technique for incremental aggregated ...
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SUCAG: Stochastic Unbiased Curvatureaided Gradient Method for Distributed Optimization
We propose and analyze a new stochastic gradient method, which we call S...
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Curvatureaided Incremental Aggregated Gradient Method
We propose a new algorithm for finite sum optimization which we call the...
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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 ...
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Active Sensing of Social Networks
This paper develops an active sensing method to estimate the relative we...
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On the Online FrankWolfe Algorithms for Convex and Nonconvex Optimizations
In this paper, the online variants of the classical FrankWolfe algorith...
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