
Stochastic Alternating Direction Method of Multipliers for ByzantineRobust Distributed Learning
This paper aims to solve a distributed learning problem under Byzantine ...
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BROADCAST: Reducing Both Stochastic and Compression Noise to Robustify CommunicationEfficient Federated Learning
Communication between workers and the master node to collect local stoch...
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ByzantineRobust VarianceReduced Federated Learning over Distributed Noni.i.d. Data
We propose a Byzantinerobust variancereduced stochastic gradient desce...
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ByzantineRobust Decentralized Stochastic Optimization over Static and TimeVarying Networks
In this paper, we consider the Byzantinerobust stochastic optimization ...
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Conditional Augmentation for Aspect Term Extraction via Masked SequencetoSequence Generation
Aspect term extraction aims to extract aspect terms from review texts as...
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HierTrain: Fast Hierarchical Edge AI Learning with Hybrid Parallelism in MobileEdgeCloud Computing
Nowadays, deep neural networks (DNNs) are the core enablers for many eme...
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Federated VarianceReduced Stochastic Gradient Descent with Robustness to Byzantine Attacks
This paper deals with distributed finitesum optimization for learning o...
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Convolutional Neural Networks for SpaceTime Block Coding Recognition
We find that the latest advances in machine learning with deep neural ne...
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CommunicationCensored Linearized ADMM for Decentralized Consensus Optimization
In this paper, we propose a communication and computationefficient alg...
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CommunicationCensored Distributed Stochastic Gradient Descent
This paper develops a communicationefficient algorithm to solve the sto...
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Asynchronous Stochastic Composition Optimization with Variance Reduction
Composition optimization has drawn a lot of attention in a wide variety ...
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RSA: ByzantineRobust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
In this paper, we propose a class of robust stochastic subgradient metho...
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Solving Nonsmooth Constrained Programs with Lower Complexity than O(1/ε): A PrimalDual Homotopy Smoothing Approach
We propose a new primaldual homotopy smoothing algorithm for a linearly...
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DADA: Deep Adversarial Data Augmentation for Extremely Low Data Regime Classification
Deep learning has revolutionized the performance of classification, but ...
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An Online Convex Optimization Approach to Dynamic Network Resource Allocation
Existing approaches to online convex optimization (OCO) make sequential ...
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Stacked Approximated Regression Machine: A Simple Deep Learning Approach
With the agreement of my coauthors, I Zhangyang Wang would like to withd...
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Make Workers Work Harder: Decoupled Asynchronous Proximal Stochastic Gradient Descent
Asynchronous parallel optimization algorithms for solving largescale ma...
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Learning A Deep ℓ_∞ Encoder for Hashing
We investigate the ℓ_∞constrained representation which demonstrates rob...
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D^3: Deep DualDomain Based Fast Restoration of JPEGCompressed Images
In this paper, we design a Deep DualDomain (D^3) based fast restoration...
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Learning Deep ℓ_0 Encoders
Despite its nonconvex nature, ℓ_0 sparse approximation is desirable in m...
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Decentralized learning for wireless communications and networking
This chapter deals with decentralized learning algorithms for innetwork...
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Qing Ling
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