53 citations · 293 across the 32 of their papers we have counts for
36 papers
A Renderer-Enabled Framework for Computing Parameter Estimation Lower Bounds in Plenoptic Imaging Systems
Abhinav V. Sambasivan, Liam J. Coulter, Richard G. Paxman +1
This work focuses on assessing the information-theoretic limits of scene parameter estimation in plenoptic imaging systems. A general framework to compute lower bounds on the param…
Online Stochastic Gradient Descent Learns Linear Dynamical Systems from A Single Trajectory
Navid Reyhanian, Jarvis Haupt
This work investigates the problem of estimating the weight matrices of a stable time-invariant linear dynamical system from a single sequence of noisy measurements. We show that i…
Convexifying Sparse Interpolation with Infinitely Wide Neural Networks: An Atomic Norm Approach
Akshay Kumar, Jarvis Haupt
This work examines the problem of exact data interpolation via sparse (neuron count), infinitely wide, single hidden layer neural networks with leaky rectified linear unit activati…
Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We consider the problem of factorizing a structured 3-way tensor into its constituent Canonical Polyadic (CP) factors. This decomposition, which can be viewed as a generalization o…
A Provably Communication-Efficient Asynchronous Distributed Inference Method for Convex and Nonconvex Problems
Jineng Ren, Jarvis Haupt
This paper proposes and analyzes a communication-efficient distributed optimization framework for general nonconvex nonsmooth signal processing and machine learning problems under…
Target-based Hyperspectral Demixing via Generalized Robust PCA
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
Localizing targets of interest in a given hyperspectral (HS) image has applications ranging from remote sensing to surveillance. This task of target detection leverages the fact th…
A Dictionary-Based Generalization of Robust PCA Part II: Applications to Hyperspectral Demixing
Sirisha Rambhatla, Xingguo Li, Jineng Ren +1
We consider the task of localizing targets of interest in a hyperspectral (HS) image based on their spectral signature(s), by posing the problem as two distinct convex demixing tas…
TensorMap: Lidar-Based Topological Mapping and Localization via Tensor Decompositions
Sirisha Rambhatla, Nikos D. Sidiropoulos, Jarvis Haupt
We propose a technique to develop (and localize in) topological maps from light detection and ranging (Lidar) data. Localizing an autonomous vehicle with respect to a reference map…
A Dictionary Based Generalization of Robust PCA
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We analyze the decomposition of a data matrix, assumed to be a superposition of a low-rank component and a component which is sparse in a known dictionary, using a convex demixing…
NOODL: Provable Online Dictionary Learning and Sparse Coding
Sirisha Rambhatla, Xingguo Li, Jarvis Haupt
We consider the dictionary learning problem, where the aim is to model the given data as a linear combination of a few columns of a matrix known as a dictionary, where the sparse w…
A Dictionary-Based Generalization of Robust PCA with Applications to Target Localization in Hyperspectral Imaging
Sirisha Rambhatla, Xingguo Li, Jineng Ren +1
We consider the decomposition of a data matrix assumed to be a superposition of a low-rank matrix and a component which is sparse in a known dictionary, using a convex demixing met…
On Tighter Generalization Bound for Deep Neural Networks: CNNs, ResNets, and Beyond
Xingguo Li, Junwei Lu, Zhaoran Wang +2
We establish a margin based data dependent generalization error bound for a general family of deep neural networks in terms of the depth and width, as well as the Jacobian of the n…
On Landscape of Lagrangian Functions and Stochastic Search for Constrained Nonconvex Optimization
Zhehui Chen, Xingguo Li, Lin F. Yang +2
We study constrained nonconvex optimization problems in machine learning, signal processing, and stochastic control. It is well-known that these problems can be rewritten to a mini…
Near Optimal Sketching of Low-Rank Tensor Regression
Jarvis Haupt, Xingguo Li, David P. Woodruff
We study the least squares regression problem \begin{align*} \min_{Θ\in \mathcal{S}_{\odot D,R}} \|AΘ-b\|_2, \end{align*} where is the set of for whic…
Communication-efficient Algorithm for Distributed Sparse Learning via Two-way Truncation
Jineng Ren, Jarvis Haupt
We propose a communicationally and computationally efficient algorithm for high-dimensional distributed sparse learning. At each iteration, local machines compute the gradient on l…
Improved Support Recovery Guarantees for the Group Lasso With Applications to Structural Health Monitoring
Mojtaba Kadkhodaie Elyaderani, Swayambhoo Jain, Jeffrey Druce +2
This paper considers the problem of estimating an unknown high dimensional signal from noisy linear measurements, {when} the signal is assumed to possess a \emph{group-sparse} stru…
On Quadratic Convergence of DC Proximal Newton Algorithm for Nonconvex Sparse Learning in High Dimensions
Xingguo Li, Lin F. Yang, Jason Ge +3
We propose a DC proximal Newton algorithm for solving nonconvex regularized sparse learning problems in high dimensions. Our proposed algorithm integrates the proximal Newton algor…
Noisy Tensor Completion for Tensors with a Sparse Canonical Polyadic Factor
Swayambhoo Jain, Alexander Gutierrez, Jarvis Haupt
In this paper we study the problem of noisy tensor completion for tensors that admit a canonical polyadic or CANDECOMP/PARAFAC (CP) decomposition with one of the factors being spar…
Robust Low-Complexity Randomized Methods for Locating Outliers in Large Matrices
Xingguo Li, Jarvis Haupt
This paper examines the problem of locating outlier columns in a large, otherwise low-rank matrix, in settings where {}{the data} are noisy, or where the overall matrix has missing…
Symmetry, Saddle Points, and Global Optimization Landscape of Nonconvex Matrix Factorization
Xingguo Li, Junwei Lu, Raman Arora +4
We propose a general theory for studying the \xl{landscape} of nonconvex \xl{optimization} with underlying symmetric structures \tz{for a class of machine learning problems (e.g.,…
On Fast Convergence of Proximal Algorithms for SQRT-Lasso Optimization: Don't Worry About Its Nonsmooth Loss Function
Xingguo Li, Haoming Jiang, Jarvis Haupt +4
Many machine learning techniques sacrifice convenient computational structures to gain estimation robustness and modeling flexibility. However, by exploring the modeling structures…
Nonconvex Sparse Learning via Stochastic Optimization with Progressive Variance Reduction
Xingguo Li, Raman Arora, Han Liu +2
We propose a stochastic variance reduced optimization algorithm for solving sparse learning problems with cardinality constraints. Sufficient conditions are provided, under which t…
A Compressed Sensing Based Decomposition of Electrodermal Activity Signals
Swayambhoo Jain, Urvashi Oswal, Kevin S. Xu +2
The measurement and analysis of Electrodermal Activity (EDA) offers applications in diverse areas ranging from market research, to seizure detection, to human stress analysis. Unfo…
Minimax Lower Bounds for Noisy Matrix Completion Under Sparse Factor Models
Abhinav V. Sambasivan, Jarvis D. Haupt
This paper examines fundamental error characteristics for a general class of matrix completion problems, where the matrix of interest is a product of two a priori unknown matrices,…