
FiniteTime Analysis of Decentralized Stochastic Approximation with Applications in MultiAgent and MultiTask Learning
Stochastic approximation, a datadriven approach for finding the fixed p...
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Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
The attention mechanism has demonstrated superior performance for infere...
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Sample complexity and effective dimension for regression on manifolds
We consider the theory of regression on a manifold using reproducing ker...
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A Decentralized Policy Gradient Approach to Multitask Reinforcement Learning
We develop a mathematical framework for solving multitask reinforcement...
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FiniteTime Analysis of Stochastic Gradient Descent under Markov Randomness
Motivated by broad applications in reinforcement learning and machine le...
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Localized sketching for matrix multiplication and ridge regression
We consider sketched approximate matrix multiplication and ridge regress...
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Hardwareaware Pruning of DNNs using LFSRGenerated PseudoRandom Indices
Deep neural networks (DNNs) have been emerged as the stateoftheart al...
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Phase Retrieval of LowRank Matrices by Anchored Regression
We study the lowrank phase retrieval problem, where we try to recover a...
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Convex Programming for Estimation in Nonlinear Recurrent Models
We propose a formulation for nonlinear recurrent models that includes si...
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FiniteTime Performance of Distributed Temporal Difference Learning with Linear Function Approximation
We study the policy evaluation problem in multiagent reinforcement lear...
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Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables
Deep learning models have significantly improved the visual quality and ...
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Appearancebased Gesture recognition in the compressed domain
We propose a novel appearancebased gesture recognition algorithm using ...
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Trading beams for bandwidth: Imaging with randomized beamforming
We study the problem of actively imaging a rangelimited farfield scene...
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Fast Convex Pruning of Deep Neural Networks
We develop a fast, tractable technique called NetTrim for simplifying a...
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ROAST: Rapid Orthogonal Approximate Slepian Transform
In this paper, we provide a Rapid Orthogonal Approximate Slepian Transfo...
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Solving Equations of Random Convex Functions via Anchored Regression
We consider the question of estimating a solution to a system of equatio...
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NetTrim: Convex Pruning of Deep Neural Networks with Performance Guarantee
We introduce and analyze a new technique for model reduction for deep ne...
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Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
We propose a flexible convex relaxation for the phase retrieval problem ...
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A Lightpowered, AlwaysOn, Smart Camera with Compressed Domain Gesture Detection
In this paper we propose an energyefficient camerabased gesture recogn...
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Sweep Distortion Removal from THz Images via Blind Demodulation
Heavy sweep distortion induced by alignments and interreflections of la...
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Learning Shapes by Convex Composition
We present a mathematical and algorithmic scheme for learning the princi...
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Sparse Recovery of Streaming Signals Using L1Homotopy
Most of the existing methods for sparse signal recovery assume a static ...
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Fast and Accurate Algorithms for ReWeighted L1Norm Minimization
To recover a sparse signal from an underdetermined system, we often solv...
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Justin Romberg
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