101 citations · 615 across the 44 of their papers we have counts for
51 papers
Truncated Matrix Completion - An Empirical Study
Rishhabh Naik, Nisarg Trivedi, Davoud Ataee Tarzanagh +1
Low-rank Matrix Completion (LRMC) describes the problem where we wish to recover missing entries of partially observed low-rank matrix. Most existing matrix completion work deals w…
Streaming Heteroscedastic Probabilistic PCA with Missing Data
Kyle Gilman, David Hong, Jeffrey A. Fessler +1
Streaming principal component analysis (PCA) is an integral tool in large-scale machine learning for rapidly estimating low-dimensional subspaces from very high-dimensional data ar…
Matrix Completion over Finite Fields: Bounds and Belief Propagation Algorithms
Mahdi Soleymani, Qiang Liu, Hessam Mahdavifar +1
We consider the low rank matrix completion problem over finite fields. This problem has been extensively studied in the domain of real/complex numbers, however, to the best of auth…
Learning physics-based reduced-order models from data using nonlinear manifolds
Rudy Geelen, Laura Balzano, Stephen Wright +1
We present a novel method for learning reduced-order models of dynamical systems using nonlinear manifolds. First, we learn the manifold by identifying nonlinear structure in the d…
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization
Javier Salazar Cavazos, Jeffrey A. Fessler, Laura Balzano
Principal component analysis (PCA) is a key tool in the field of data dimensionality reduction that is useful for various data science problems. However, many applications involve…
Learning latent representations in high-dimensional state spaces using polynomial manifold constructions
Rudy Geelen, Laura Balzano, Karen Willcox
We present a novel framework for learning cost-efficient latent representations in problems with high-dimensional state spaces through nonlinear dimension reduction. By enriching l…
Dynamic Subspace Estimation with Grassmannian Geodesics
Cameron J. Blocker, Haroon Raja, Jeffrey A. Fessler +1
Dynamic subspace estimation, or subspace tracking, is a fundamental problem in statistical signal processing and machine learning. This paper considers a geodesic model for time-va…
HeMPPCAT: Mixtures of Probabilistic Principal Component Analysers for Data with Heteroscedastic Noise
Alec S. Xu, Laura Balzano, Jeffrey A. Fessler
Mixtures of probabilistic principal component analysis (MPPCA) is a well-known mixture model extension of principal component analysis (PCA). Similar to PCA, MPPCA assumes the data…
A Proximal DC Algorithm for Sample Average Approximation of Chance Constrained Programming
Peng Wang, Rujun Jiang, Qingyuan Kong +1
Chance constrained programming (CCP) refers to a type of optimization problem with uncertain constraints that are satisfied with at least a prescribed probability level. In this wo…
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold
Can Yaras, Peng Wang, Zhihui Zhu +2
When training overparameterized deep networks for classification tasks, it has been widely observed that the learned features exhibit a so-called "neural collapse" phenomenon. More…
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods
Davoud Ataee Tarzanagh, Parvin Nazari, Bojian Hou +2
This paper introduces \textit{online bilevel optimization} in which a sequence of time-varying bilevel problems is revealed one after the other. We extend the known regret bounds f…
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering
Peng Wang, Huikang Liu, Anthony Man-Cho So +1
The K-subspaces (KSS) method is a generalization of the K-means method for subspace clustering. In this work, we present local convergence analysis and a recovery guarantee for KSS…
A Semidefinite Relaxation for Sums of Heterogeneous Quadratic Forms on the Stiefel Manifold
Kyle Gilman, Sam Burer, Laura Balzano
We study the maximization of sums of heterogeneous quadratic forms over the Stiefel manifold, a nonconvex problem that arises in several modern signal processing and machine learni…
Mode Reduction for Markov Jump Systems
Zhe Du, Laura Balzano, Necmiye Ozay
Switched systems are capable of modeling processes with underlying dynamics that may change abruptly over time. To achieve accurate modeling in practice, one may need a large numbe…
Fair Community Detection and Structure Learning in Heterogeneous Graphical Models
Davoud Ataee Tarzanagh, Laura Balzano, Alfred O. Hero
Inference of community structure in probabilistic graphical models may not be consistent with fairness constraints when nodes have demographic attributes. Certain demographics may…
Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
Yahya Sattar, Zhe Du, Davoud Ataee Tarzanagh +3
Learning how to effectively control unknown dynamical systems is crucial for intelligent autonomous systems. This task becomes a significant challenge when the underlying dynamics…
Certainty Equivalent Quadratic Control for Markov Jump Systems
Zhe Du, Yahya Sattar, Davoud Ataee Tarzanagh +3
Real-world control applications often involve complex dynamics subject to abrupt changes or variations. Markov jump linear systems (MJS) provide a rich framework for modeling such…
HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
David Hong, Kyle Gilman, Laura Balzano +1
Principal component analysis (PCA) is a classical and ubiquitous method for reducing data dimensionality, but it is suboptimal for heterogeneous data that are increasingly common i…
Supervised PCA: A Multiobjective Approach
Alexander Ritchie, Laura Balzano, Daniel Kessler +2
Methods for supervised principal component analysis (SPCA) aim to incorporate label information into principal component analysis (PCA), so that the extracted features are more use…
Preference Modeling with Context-Dependent Salient Features
Amanda Bower, Laura Balzano
We consider the problem of estimating a ranking on a set of items from noisy pairwise comparisons given item features. We address the fact that pairwise comparison data often refle…
Grassmannian Optimization for Online Tensor Completion and Tracking with the t-SVD
Kyle Gilman, Davoud Ataee Tarzanagh, Laura Balzano
We propose a new fast streaming algorithm for the tensor completion problem of imputing missing entries of a low-tubal-rank tensor using the tensor singular value decomposition (t-…
Online matrix factorization for Markovian data and applications to Network Dictionary Learning
Hanbaek Lyu, Deanna Needell, Laura Balzano
Online Matrix Factorization (OMF) is a fundamental tool for dictionary learning problems, giving an approximate representation of complex data sets in terms of a reduced number of…
Mode Clustering for Markov Jump Systems
Zhe Du, Necmiye Ozay, Laura Balzano
In this work, we consider the problem of mode clustering in Markov jump models. This model class consists of multiple dynamical modes with a switching sequence that determines how…
A Memory-efficient Algorithm for Large-scale Sparsity Regularized Image Reconstruction
Greg Ongie, Naveen Murthy, Laura Balzano +1
We derive a memory-efficient first-order variable splitting algorithm for convex image reconstruction problems with non-smooth regularization terms. The algorithm is based on a pri…