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Found 36 papers, 5 papers with code
Subspace Tracking with Dynamical Models on the Grassmannian
Tracking signals in dynamic environments presents difficulties in both analysis and implementation.
Slepian Beamforming: Broadband Beamforming using Streaming Least Squares
Alongside a careful discussion of this model and how to choose its parameters we show how to fit the model to new blocks of samples as they are received, producing a streaming output.
Decentralized and Privacy-Preserving Learning of Approximate Stackelberg Solutions in Energy Trading Games with Demand Response Aggregators
In this work, a novel Stackelberg game theoretic framework is proposed for trading energy bidirectionally between the demand-response (DR) aggregator and the prosumers.
Iterative Broadband Source Localization
We show that by using these models, our adapted algorithms can successfully localize broadband sources under a variety of adverse operating scenarios.
Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solver
In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements.
Streaming Reconstruction from Non-uniform Samples
We present an online algorithm for reconstructing a signal from a set of non-uniform samples.
Broadband Beamforming via Linear Embedding
In modern applications multi-sensor arrays are subject to an ever-present demand to accommodate signals with higher bandwidths.
Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games
We study the problem of finding the Nash equilibrium in a two-player zero-sum Markov game.
Decentralized Feature-Distributed Optimization for Generalized Linear Models
We consider the "all-for-one" decentralized learning problem for generalized linear models.
Finite-Time Complexity of Online Primal-Dual Natural Actor-Critic Algorithm for Constrained Markov Decision Processes
To solve this constrained optimization program, we study an online actor-critic variant of a classic primal-dual method where the gradients of both the primal and dual functions are estimated using samples from a single trajectory generated by the underlying time-varying Markov processes.
A Two-Time-Scale Stochastic Optimization Framework with Applications in Control and Reinforcement Learning
In our two-time-scale approach, one scale is to estimate the true gradient from these samples, which is then used to update the estimate of the optimal solution.
Thomson's Multitaper Method Revisited
This is useful in problems where many samples are taken, and thus, using many tapers is desirable.
Finite Sample Analysis of Two-Time-Scale Natural Actor-Critic Algorithm
In this paper, we characterize the \emph{global} convergence of an online natural actor-critic algorithm in the tabular setting using a single trajectory of samples.
Finite-Time Convergence Rates of Decentralized Stochastic Approximation with Applications in Multi-Agent and Multi-Task Learning
We study a decentralized variant of stochastic approximation, a data-driven approach for finding the root of an operator under noisy measurements.
Fast Graph Attention Networks Using Effective Resistance Based Graph Sparsification
The attention mechanism has demonstrated superior performance for inference over nodes in graph neural networks (GNNs), however, they result in a high computational burden during both training and inference.
Sample complexity and effective dimension for regression on manifolds
We consider the theory of regression on a manifold using reproducing kernel Hilbert space methods.
A Decentralized Policy Gradient Approach to Multi-task Reinforcement Learning
We develop a mathematical framework for solving multi-task reinforcement learning (MTRL) problems based on a type of policy gradient method.
Finite-Time Analysis of Stochastic Gradient Descent under Markov Randomness
Motivated by broad applications in reinforcement learning and machine learning, this paper considers the popular stochastic gradient descent (SGD) when the gradients of the underlying objective function are sampled from Markov processes.
Localized sketching for matrix multiplication and ridge regression
We consider sketched approximate matrix multiplication and ridge regression in the novel setting of localized sketching, where at any given point, only part of the data matrix is available.
Decentralized sketching of low rank matrices
We address a low-rank matrix recovery problem where each column of a rank-r matrix X of size (d1, d2) is compressed beyond the point of recovery to size L with L << d1.
Hardware-aware Pruning of DNNs using LFSR-Generated Pseudo-Random Indices
Deep neural networks (DNNs) have been emerged as the state-of-the-art algorithms in broad range of applications.
Convex Programming for Estimation in Nonlinear Recurrent Models
We propose a formulation for nonlinear recurrent models that includes simple parametric models of recurrent neural networks as a special case.
Finite-Time Performance of Distributed Temporal Difference Learning with Linear Function Approximation
Our main contribution is to provide a finite-analysis on the performance of this distributed {\sf TD}$(\lambda)$ algorithm for both constant and time-varying step sizes.
Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation for Multi-Agent Reinforcement Learning
In this problem, a group of agents works cooperatively to evaluate the value function for the global discounted accumulative reward problem, which is composed of local rewards observed by the agents.
Optimization and Control
Fast Compressive Sensing Recovery Using Generative Models with Structured Latent Variables
Deep learning models have significantly improved the visual quality and accuracy on compressive sensing recovery.
Appearance-based Gesture recognition in the compressed domain
We propose a novel appearance-based gesture recognition algorithm using compressed domain signal processing techniques.
Fast Convex Pruning of Deep Neural Networks
We develop a fast, tractable technique called Net-Trim for simplifying a trained neural network.
Solving Equations of Random Convex Functions via Anchored Regression
We consider the question of estimating a solution to a system of equations that involve convex nonlinearities, a problem that is common in machine learning and signal processing.
Net-Trim: Convex Pruning of Deep Neural Networks with Performance Guarantee
This program seeks a sparse set of weights at each layer that keeps the layer inputs and outputs consistent with the originally trained model.
Phase Retrieval Meets Statistical Learning Theory: A Flexible Convex Relaxation
We propose a flexible convex relaxation for the phase retrieval problem that operates in the natural domain of the signal.
A Light-powered, Always-On, Smart Camera with Compressed Domain Gesture Detection
In this paper we propose an energy-efficient camera-based gesture recognition system powered by light energy for "always on" applications.
Sweep Distortion Removal from THz Images via Blind Demodulation
Heavy sweep distortion induced by alignments and inter-reflections of layers of a sample is a major burden in recovering 2D and 3D information in time resolved spectral imaging.
Learning Shapes by Convex Composition
We present a mathematical and algorithmic scheme for learning the principal geometric elements in an image or 3D object.
Sparse Recovery of Streaming Signals Using L1-Homotopy
In this paper, we discuss two such streaming systems and a homotopy-based algorithm for quickly solving the associated L1-norm minimization programs: 1) Recovery of a smooth, time-varying signal for which, instead of using block transforms, we use lapped orthogonal transforms for sparse representation.
Blind Deconvolution using Convex Programming
That is, we show that if $\boldsymbol{x}$ is drawn from a random subspace of dimension $N$, and $\boldsymbol{w}$ is a vector in a subspace of dimension $K$ whose basis vectors are "spread out" in the frequency domain, then nuclear norm minimization recovers $\boldsymbol{w}\boldsymbol{x}^*$ without error.
Information Theory Information Theory
Dynamic Updating for L1 Minimization
We consider cases where the underlying signal changes slightly between measurements, and where new measurements of a fixed signal are sequentially added to the system.
Information Theory Information Theory
