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04/04/2023
Scalable Online Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators
In this work, a Stackelberg game theoretic framework is proposed for tra...
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03/23/2023
Connected Superlevel Set in (Deep) Reinforcement Learning and its Application to Minimax Theorems
The aim of this paper is to improve the understanding of the optimizatio...
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10/10/2022
Loop Unrolled Shallow Equilibrium Regularizer (LUSER) – A Memory-Efficient Inverse Problem Solver
In inverse problems we aim to reconstruct some underlying signal of inte...
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07/23/2022
A Dual Accelerated Method for Online Stochastic Distributed Averaging: From Consensus to Decentralized Policy Evaluation
Motivated by decentralized sensing and policy evaluation problems, we co...
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05/27/2022
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
We study the problem of finding the Nash equilibrium in a two-player zer...
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10/28/2021
Decentralized Feature-Distributed Optimization for Generalized Linear Models
We consider the "all-for-one" decentralized learning problem for general...
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10/28/2021
Approximately low-rank recovery from noisy and local measurements by convex program
Low-rank matrix models have been universally useful for numerous applica...
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10/21/2021
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes
We consider a discounted cost constrained Markov decision process (CMDP)...
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09/29/2021
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
We study a novel two-time-scale stochastic gradient method for solving o...
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03/22/2021
Thomson's Multitaper Method Revisited
Thomson's multitaper method estimates the power spectrum of a signal fro...
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01/26/2021
Finite Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm
Actor-critic style two-time-scale algorithms are very popular in reinfor...
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10/28/2020
Finite-Time Analysis of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning
Stochastic approximation, a data-driven approach for finding the fixed p...
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06/15/2020
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
The attention mechanism has demonstrated superior performance for infere...
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06/13/2020
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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06/08/2020
A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning
We develop a mathematical framework for solving multi-task reinforcement...
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03/24/2020
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness
Motivated by broad applications in reinforcement learning and machine le...
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03/20/2020
Localized sketching for matrix multiplication and ridge regression
We consider sketched approximate matrix multiplication and ridge regress...
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11/09/2019
Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random Indices
Deep neural networks (DNNs) have been emerged as the state-of-the-art al...
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10/25/2019
Phase Retrieval of Low-Rank Matrices by Anchored Regression
We study the low-rank phase retrieval problem, where we try to recover a...
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08/26/2019
Convex Programming for Estimation in Nonlinear Recurrent Models
We propose a formulation for nonlinear recurrent models that includes si...
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07/25/2019
Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation
We study the policy evaluation problem in multi-agent reinforcement lear...
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02/19/2019
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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02/19/2019
Appearance-based Gesture recognition in the compressed domain
We propose a novel appearance-based gesture recognition algorithm using ...
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01/31/2019
Trading beams for bandwidth: Imaging with randomized beamforming
We study the problem of actively imaging a range-limited far-field scene...
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06/17/2018
Fast Convex Pruning of Deep Neural Networks
We develop a fast, tractable technique called Net-Trim for simplifying a...
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12/13/2017
ROAST: Rapid Orthogonal Approximate Slepian Transform
In this paper, we provide a Rapid Orthogonal Approximate Slepian Transfo...
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02/17/2017
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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11/16/2016
Net-Trim: 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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10/13/2016
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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05/26/2016
A Light-powered, Always-On, Smart Camera with Compressed Domain Gesture Detection
In this paper we propose an energy-efficient camera-based gesture recogn...
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03/29/2016
Sweep Distortion Removal from THz Images via Blind Demodulation
Heavy sweep distortion induced by alignments and inter-reflections of la...
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02/23/2016
Learning Shapes by Convex Composition
We present a mathematical and algorithmic scheme for learning the princi...
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06/14/2013
Sparse Recovery of Streaming Signals Using L1-Homotopy
Most of the existing methods for sparse signal recovery assume a static ...
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08/03/2012