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08/05/2023
Structured Low-Rank Tensors for Generalized Linear Models
Recent works have shown that imposing tensor structures on the coefficie...
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07/13/2023
Accelerated gradient methods for nonconvex optimization: Escape trajectories from strict saddle points and convergence to local minima
This paper considers the problem of understanding the behavior of a gene...
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12/15/2021
Domain-informed neural networks for interaction localization within astroparticle experiments
This work proposes a domain-informed neural network architecture for exp...
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08/27/2021
A Guide to Reproducible Research in Signal Processing and Machine Learning
Reproducibility is a growing problem that has been extensively studied a...
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08/27/2021
FAST-PCA: A Fast and Exact Algorithm for Distributed Principal Component Analysis
Principal Component Analysis (PCA) is a fundamental data preprocessing t...
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06/25/2021
A hybrid model-based and learning-based approach for classification using limited number of training samples
The fundamental task of classification given a limited number of trainin...
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05/31/2021
A Minimax Lower Bound for Low-Rank Matrix-Variate Logistic Regression
This paper considers the problem of matrix-variate logistic regression. ...
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03/11/2021
Distributed Principal Subspace Analysis for Partitioned Big Data: Algorithms, Analysis, and Implementation
Principal Subspace Analysis (PSA) is one of the most popular approaches ...
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01/07/2021
Boundary Conditions for Linear Exit Time Gradient Trajectories Around Saddle Points: Analysis and Algorithm
Gradient-related first-order methods have become the workhorse of large-...
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01/05/2021
A Linearly Convergent Algorithm for Distributed Principal Component Analysis
Principal Component Analysis (PCA) is the workhorse tool for dimensional...
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06/01/2020
Exit Time Analysis for Approximations of Gradient Descent Trajectories Around Saddle Points
This paper considers the problem of understanding the exit time for traj...
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05/18/2020
Scaling-up Distributed Processing of Data Streams for Machine Learning
Emerging applications of machine learning in numerous areas involve cont...
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02/26/2020
Learning Product Graphs Underlying Smooth Graph Signals
Real-world data is often times associated with irregular structures that...
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01/04/2020
Distributed Stochastic Algorithms for High-rate Streaming Principal Component Analysis
This paper considers the problem of estimating the principal eigenvector...
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11/09/2019
Tensor Regression Using Low-rank and Sparse Tucker Decompositions
This paper studies a tensor-structured linear regression model with a sc...
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08/23/2019
Adversary-resilient Inference and Machine Learning: From Distributed to Decentralized
While the last few decades have witnessed a huge body of work devoted to...
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08/21/2019
BRIDGE: Byzantine-resilient Decentralized Gradient Descent
Decentralized optimization techniques are increasingly being used to lea...
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08/01/2019
Learning-Aided Physical Layer Attacks Against Multicarrier Communications in IoT
Internet-of-Things (IoT) devices that are limited in power and processin...
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03/22/2019
Learning Mixtures of Separable Dictionaries for Tensor Data: Analysis and Algorithms
This work addresses the problem of learning sparse representations of te...
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12/10/2017
Identifiability of Kronecker-structured Dictionaries for Tensor Data
This paper derives sufficient conditions for reliable recovery of coordi...
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11/13/2017
STARK: Structured Dictionary Learning Through Rank-one Tensor Recovery
In recent years, a class of dictionaries have been proposed for multidim...
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08/28/2017
ByRDiE: Byzantine-resilient distributed coordinate descent for decentralized learning
Distributed machine learning algorithms enable processing of datasets th...
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08/21/2017
ExSIS: Extended Sure Independence Screening for Ultrahigh-dimensional Linear Models
Statistical inference can be computationally prohibitive in ultrahigh-di...
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04/25/2017
Stochastic Optimization from Distributed, Streaming Data in Rate-limited Networks
Motivated by machine learning applications in networks of sensors, inter...
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12/23/2016
Human Action Attribute Learning From Video Data Using Low-Rank Representations
Representation of human actions as a sequence of human body movements or...
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05/17/2016
Minimax Lower Bounds for Kronecker-Structured Dictionary Learning
Dictionary learning is the problem of estimating the collection of atomi...
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12/25/2014
Cloud K-SVD: A Collaborative Dictionary Learning Algorithm for Big, Distributed Data
This paper studies the problem of data-adaptive representations for big,...
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12/21/2014
Learning the nonlinear geometry of high-dimensional data: Models and algorithms
Modern information processing relies on the axiom that high-dimensional ...
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10/08/2012