
Only Train Once: A OneShot Neural Network Training And Pruning Framework
Structured pruning is a commonly used technique in deploying deep neural...
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Tighter Analysis of Alternating Stochastic Gradient Method for Stochastic Nested Problems
Stochastic nested optimization, including stochastic compositional, min...
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A SingleTimescale Stochastic Bilevel Optimization Method
Stochastic bilevel optimization generalizes the classic stochastic optim...
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Asynchronous Advantage Actor Critic: Nonasymptotic Analysis and Linear Speedup
Asynchronous and parallel implementation of standard reinforcement learn...
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CADA: CommunicationAdaptive Distributed Adam
Stochastic gradient descent (SGD) has taken the stage as the primary wor...
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Hybrid Federated Learning: Algorithms and Implementation
Federated learning (FL) is a recently proposed distributed machine learn...
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Automatic Clustering for Unsupervised Risk Diagnosis of Vehicle Driving for Smart Road
Early risk diagnosis and driving anomaly detection from vehicle stream a...
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Neural Network Compression Via Sparse Optimization
The compression of deep neural networks (DNNs) to reduce inference cost ...
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A Simple Spectral Failure Mode for Graph Convolutional Networks
We present a simple generative model in which spectral graph embedding f...
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Solving Stochastic Compositional Optimization is Nearly as Easy as Solving Stochastic Optimization
Stochastic compositional optimization generalizes classic (noncompositi...
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Recurrent Exposure Generation for LowLight Face Detection
Face detection from lowlight images is challenging due to limited photo...
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VAFL: a Method of Vertical Asynchronous Federated Learning
Horizontal Federated learning (FL) handles multiclient data that share ...
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CommunicationEfficient Robust Federated Learning Over Heterogeneous Datasets
This work investigates faultresilient federated learning when the data ...
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Orthant Based Proximal Stochastic Gradient Method for ℓ_1Regularized Optimization
Sparsityinducing regularization problems are ubiquitous in machine lear...
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LASG: Lazily Aggregated Stochastic Gradients for CommunicationEfficient Distributed Learning
This paper targets solving distributed machine learning problems such as...
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Adaptive Temporal Difference Learning with Linear Function Approximation
This paper revisits the celebrated temporal difference (TD) learning alg...
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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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Decentralized Markov Chain Gradient Descent
Decentralized stochastic gradient method emerges as a promising solution...
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CommunicationEfficient Distributed Learning via Lazily Aggregated Quantized Gradients
The present paper develops a novel aggregated gradient approach for dist...
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CommunicationCensored Distributed Stochastic Gradient Descent
This paper develops a communicationefficient algorithm to solve the sto...
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Generative Adversarial Network for Handwritten Text
Generative adversarial networks (GANs) has proven hugely successful in v...
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Novel Dense Subgraph Discovery Primitives: Risk Aversion and Exclusion Queries
In the densest subgraph problem, given a weighted undirected graph G(V,E...
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CommunicationEfficient Distributed Reinforcement Learning
This paper studies the distributed reinforcement learning (DRL) problem ...
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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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Learning and Management for InternetofThings: Accounting for Adaptivity and Scalability
InternetofThings (IoT) envisions an intelligent infrastructure of netw...
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Delayed Bandit Online Learning with Unknown Delays
This paper studies bandit learning problems with delayed feedback, which...
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LAG: Lazily Aggregated Gradient for CommunicationEfficient Distributed Learning
This paper presents a new class of gradient methods for distributed mach...
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Secure Mobile Edge Computing in IoT via Collaborative Online Learning
To accommodate heterogeneous tasks in Internet of Things (IoT), a new co...
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MultiTimescale Online Optimization of Network Function Virtualization for Service Chaining
Network Function Virtualization (NFV) can costefficiently provide netwo...
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Random Featurebased Online Multikernel Learning in Environments with Unknown Dynamics
Kernelbased methods exhibit welldocumented performance in various nonl...
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Online Ensemble Multikernel Learning Adaptive to Nonstationary and Adversarial Environments
Kernelbased methods exhibit welldocumented performance in various nonl...
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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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Stochastic Averaging for Constrained Optimization with Application to Online Resource Allocation
Existing approaches to resource allocation for nowadays stochastic netwo...
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Tianyi Chen
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