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08/21/2023
Matrix Completion over Finite Fields: Bounds and Belief Propagation Algorithms
We consider the low rank matrix completion problem over finite fields. T...
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08/05/2023
Learning physics-based reduced-order models from data using nonlinear manifolds
We present a novel method for learning reduced-order models of dynamical...
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07/06/2023
ALPCAH: Sample-wise Heteroscedastic PCA with Tail Singular Value Regularization
Principal component analysis (PCA) is a key tool in the field of data di...
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06/23/2023
Learning latent representations in high-dimensional state spaces using polynomial manifold constructions
We present a novel framework for learning cost-efficient latent represen...
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03/26/2023
Dynamic Subspace Estimation with Grassmannian Geodesics
Dynamic subspace estimation, or subspace tracking, is a fundamental prob...
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01/21/2023
HeMPPCAT: Mixtures of Probabilistic Principal Component Analysers for Data with Heteroscedastic Noise
Mixtures of probabilistic principal component analysis (MPPCA) is a well...
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09/19/2022
Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold
When training overparameterized deep networks for classification tasks, ...
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07/06/2022
Online Bilevel Optimization: Regret Analysis of Online Alternating Gradient Methods
Online optimization is a well-established optimization paradigm that aim...
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06/11/2022
Convergence and Recovery Guarantees of the K-Subspaces Method for Subspace Clustering
The K-subspaces (KSS) method is a generalization of the K-means method f...
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05/05/2022
Mode Reduction for Markov Jump Systems
Switched systems are capable of modeling processes with underlying dynam...
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12/09/2021
Fair Structure Learning in Heterogeneous Graphical Models
Inference of community structure in probabilistic graphical models may n...
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11/13/2021
Identification and Adaptive Control of Markov Jump Systems: Sample Complexity and Regret Bounds
Learning how to effectively control unknown dynamical systems is crucial...
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05/26/2021
Certainty Equivalent Quadratic Control for Markov Jump Systems
Real-world control applications often involve complex dynamics subject t...
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01/10/2021
HePPCAT: Probabilistic PCA for Data with Heteroscedastic Noise
Principal component analysis (PCA) is a classical and ubiquitous method ...
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11/10/2020
Supervised PCA: A Multiobjective Approach
Methods for supervised principal component analysis (SPCA) aim to incorp...
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02/22/2020
Preference Modeling with Context-Dependent Salient Features
We consider the problem of estimating a ranking on a set of items from n...
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01/30/2020
Grassmannian Optimization for Online Tensor Completion and Tracking in the t-SVD Algebra
We propose a new streaming algorithm, called TOUCAN, for the tensor comp...
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11/05/2019
Online matrix factorization for Markovian data and applications to Network Dictionary Learning
Online Matrix Factorization (OMF) is a fundamental tool for dictionary l...
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10/30/2018
Optimally Weighted PCA for High-Dimensional Heteroscedastic Data
Modern applications increasingly involve high-dimensional and heterogene...
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06/12/2018
Streaming PCA and Subspace Tracking: The Missing Data Case
For many modern applications in science and engineering, data are collec...
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04/26/2018
Tensor Methods for Nonlinear Matrix Completion
In the low rank matrix completion (LRMC) problem, the low rank assumptio...
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12/21/2017
Deep Unsupervised Clustering Using Mixture of Autoencoders
Unsupervised clustering is one of the most fundamental challenges in mac...
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09/14/2017
Subspace Clustering using Ensembles of K-Subspaces
We present a novel approach to the subspace clustering problem that leve...
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03/28/2017
Algebraic Variety Models for High-Rank Matrix Completion
We consider a generalization of low-rank matrix completion to the case w...
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01/16/2017
Real-Time Energy Disaggregation of a Distribution Feeder's Demand Using Online Learning
Though distribution system operators have been adding more sensors to th...
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10/12/2016
Towards a Theoretical Analysis of PCA for Heteroscedastic Data
Principal Component Analysis (PCA) is a method for estimating a subspace...
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10/01/2016
Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation From Undersampled Data
Subspace learning and matrix factorization problems have a great many ap...
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08/06/2016
Leveraging Union of Subspace Structure to Improve Constrained Clustering
Many clustering problems in computer vision and other contexts are also ...
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03/13/2016
On Learning High Dimensional Structured Single Index Models
Single Index Models (SIMs) are simple yet flexible semi-parametric model...
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12/29/2015
Matrix Completion Under Monotonic Single Index Models
Most recent results in matrix completion assume that the matrix under co...
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09/28/2015
Distance-Penalized Active Learning Using Quantile Search
Adaptive sampling theory has shown that, with proper assumptions on the ...
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06/24/2015
Global Convergence of a Grassmannian Gradient Descent Algorithm for Subspace Estimation
It has been observed in a variety of contexts that gradient descent meth...
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09/26/2013
Online Algorithms for Factorization-Based Structure from Motion
We present a family of online algorithms for real-time factorization-bas...
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07/21/2013
On GROUSE and Incremental SVD
GROUSE (Grassmannian Rank-One Update Subspace Estimation) is an incremen...
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06/03/2013
Iterative Grassmannian Optimization for Robust Image Alignment
Robust high-dimensional data processing has witnessed an exciting develo...
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12/23/2011
High-Rank Matrix Completion and Subspace Clustering with Missing Data
This paper considers the problem of completing a matrix with many missin...
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09/18/2011
Online Robust Subspace Tracking from Partial Information
This paper presents GRASTA (Grassmannian Robust Adaptive Subspace Tracki...
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04/21/2011
