458 citations · 893 across the 37 of their papers we have counts for
44 papers
Slepian Beamforming: Broadband Beamforming using Streaming Least Squares
Coleman DeLude, Mark A. Davenport, Justin Romberg
In this paper we revisit the classical problem of estimating a signal as it impinges on a multi-sensor array. We focus on the case where the impinging signal's bandwidth is appreci…
Learned Proximal Operator for Solving Seismic Deconvolution Problem
Peimeng Guan, Naveed Iqbal, Mark A. Davenport +1
Seismic deconvolution is an essential step in seismic data processing that aims to extract layer information from noisy observed traces. In general, this is an ill-posed problem wi…
New Equivalences Between Interpolation and SVMs: Kernels and Structured Features
Chiraag Kaushik, Andrew D. McRae, Mark A. Davenport +1
The support vector machine (SVM) is a supervised learning algorithm that finds a maximum-margin linear classifier, often after mapping the data to a high-dimensional feature space…
Data-Adaptive Symmetric CUSUM for Sequential Change Detection
Nauman Ahad, Mark A. Davenport, Yao Xie
Detecting change points sequentially in a streaming setting, especially when both the mean and the variance of the signal can change, is often a challenging task. A key difficulty…
Iterative Broadband Source Localization
Coleman DeLude, Rakshith Sharma, Santhosh Karnik +3
In this paper we consider the problem of localizing a set of broadband sources from a finite window of measurements. In the case of narrowband sources this can be reduced to the pr…
Loop Unrolled Shallow Equilibrium Regularizer (LUSER) -- A Memory-Efficient Inverse Problem Solver
Peimeng Guan, Jihui Jin, Justin Romberg +1
In inverse problems we aim to reconstruct some underlying signal of interest from potentially corrupted and often ill-posed measurements. Classical optimization-based techniques pr…
Broadband Beamforming via Linear Embedding
Coleman DeLude, Santhosh Karnik, Mark Davenport +1
In modern applications multi-sensor arrays are subject to an ever-present demand to accommodate signals with higher bandwidths. Standard methods for broadband beamforming, namely d…
Learning Sinkhorn divergences for supervised change point detection
Nauman Ahad, Eva L. Dyer, Keith B. Hengen +2
Many modern applications require detecting change points in complex sequential data. Most existing methods for change point detection are unsupervised and, as a consequence, lack a…
Active metric learning and classification using similarity queries
Namrata Nadagouda, Austin Xu, Mark A. Davenport
Active learning is commonly used to train label-efficient models by adaptively selecting the most informative queries. However, most active learning strategies are designed to eith…
Harmless interpolation in regression and classification with structured features
Andrew D. McRae, Santhosh Karnik, Mark A. Davenport +1
Overparametrized neural networks tend to perfectly fit noisy training data yet generalize well on test data. Inspired by this empirical observation, recent work has sought to under…
Optimal convex lifted sparse phase retrieval and PCA with an atomic matrix norm regularizer
Andrew D. McRae, Justin Romberg, Mark A. Davenport
We present novel analysis and algorithms for solving sparse phase retrieval and sparse principal component analysis (PCA) with convex lifted matrix formulations. The key innovation…
Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time
Feng Zhu, Andrew R. Sedler, Harrison A. Grier +5
Modern neural interfaces allow access to the activity of up to a million neurons within brain circuits. However, bandwidth limits often create a trade-off between greater spatial s…
Thomson's Multitaper Method Revisited
Santhosh Karnik, Justin Romberg, Mark A. Davenport
Thomson's multitaper method estimates the power spectrum of a signal from equally spaced samples by averaging tapered periodograms. Discrete prolate spheroidal sequences (D…
Semi-supervised sequence classification through change point detection
Nauman Ahad, Mark A. Davenport
Sequential sensor data is generated in a wide variety of practical applications. A fundamental challenge involves learning effective classifiers for such sequential data. While dee…
Simultaneous Preference and Metric Learning from Paired Comparisons
Austin Xu, Mark A. Davenport
A popular model of preference in the context of recommendation systems is the so-called \emph{ideal point} model. In this model, a user is represented as a vector toge…
Generative causal explanations of black-box classifiers
Matthew O'Shaughnessy, Gregory Canal, Marissa Connor +2
We develop a method for generating causal post-hoc explanations of black-box classifiers based on a learned low-dimensional representation of the data. The explanation is causal in…
Sample complexity and effective dimension for regression on manifolds
Andrew McRae, Justin Romberg, Mark Davenport
We consider the theory of regression on a manifold using reproducing kernel Hilbert space methods. Manifold models arise in a wide variety of modern machine learning problems, and…
Improved bounds for the eigenvalues of prolate spheroidal wave functions and discrete prolate spheroidal sequences
Santhosh Karnik, Justin Romberg, Mark A. Davenport
The discrete prolate spheroidal sequences (DPSSs) are a set of orthonormal sequences in which are strictly bandlimited to a frequency band and maximal…
Dynamic Knowledge embedding and tracing
Liangbei Xu, Mark A. Davenport
The goal of knowledge tracing is to track the state of a student's knowledge as it evolves over time. This plays a fundamental role in understanding the learning process and is a k…
Localized sketching for matrix multiplication and ridge regression
Rakshith S Srinivasa, Mark A Davenport, Justin Romberg
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 a…
Low-rank matrix completion and denoising under Poisson noise
Andrew D. McRae, Mark A. Davenport
This paper considers the problem of estimating a low-rank matrix from the observation of all or a subset of its entries in the presence of Poisson noise. When we observe all entrie…
Active embedding search via noisy paired comparisons
Gregory H. Canal, Andrew K. Massimino, Mark A. Davenport +1
Suppose that we wish to estimate a user's preference vector from paired comparisons of the form "does user prefer item or item ?," where both the user and items are…
Sparse Bayesian Learning with Dynamic Filtering for Inference of Time-Varying Sparse Signals
Matthew R. O'Shaughnessy, Mark A. Davenport, Christopher J. Rozell
Many signal processing applications require estimation of time-varying sparse signals, potentially with the knowledge of an imperfect dynamics model. In this paper, we propose an a…
Trading beams for bandwidth: Imaging with randomized beamforming
Rakshith Sharma Srinivasa, Mark A. Davenport, Justin Romberg
We study the problem of actively imaging a range-limited far-field scene using an antenna array. We describe how the range limit imposes structure in the measurements across multip…