Anna Ma | DeepAI

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∙ 08/31/2023

A Note on Randomized Kaczmarz Algorithm for Solving Doubly-Noisy Linear Systems

Large-scale linear systems, Ax=b, frequently arise in practice and deman...

0 El Houcine Bergou, et al. ∙

∙ 08/15/2023

On Subsampled Quantile Randomized Kaczmarz

When solving noisy linear systems Ax = b + c, the theoretical and empiri...

0 Jamie Haddock, et al. ∙

∙ 06/07/2023

Stochastic Natural Thresholding Algorithms

Sparse signal recovery is one of the most fundamental problems in variou...

0 Rachel Grotheer, et al. ∙

∙ 06/01/2023

Efficient and Robust Bayesian Selection of Hyperparameters in Dimension Reduction for Visualization

We introduce an efficient and robust auto-tuning framework for hyperpara...

0 Yin-Ting Liao, et al. ∙

∙ 06/02/2022

Robust recovery of low-rank matrices and low-tubal-rank tensors from noisy sketches

A common approach for compressing large-scale data is through matrix ske...

0 Anna Ma, et al. ∙

∙ 05/31/2022

AVIDA: Alternating method for Visualizing and Integrating Data

High-dimensional multimodal data arises in many scientific fields. The i...

0 Kathryn Dover, et al. ∙

∙ 08/30/2021

On the Number of Faces and Radii of Cells Induced by Gaussian Spherical Tessellations

We study a geometric property related to spherical hyperplane tessellati...

0 Eric Lybrand, et al. ∙

∙ 06/01/2020

Randomized Kaczmarz for Tensor Linear Systems

Solving linear systems of equations is a fundamental problem in mathemat...

0 Anna Ma, et al. ∙

∙ 12/07/2019

Greed Works: An Improved Analysis of Sampling Kaczmarz-Motkzin

Stochastic iterative algorithms have gained recent interest in machine l...

0 Jamie Haddock, et al. ∙

∙ 09/23/2019

Stochastic Iterative Hard Thresholding for Low-Tucker-Rank Tensor Recovery

Low-rank tensor recovery problems have been widely studied in many appli...

0 Rachel Grotheer, et al. ∙

∙ 08/22/2019

Iterative Hard Thresholding for Low CP-rank Tensor Models

Recovery of low-rank matrices from a small number of linear measurements...

6 Rachel Grotheer, et al. ∙

∙ 05/31/2019

Data-driven Algorithm Selection and Parameter Tuning: Two Case studies in Optimization and Signal Processing

Machine learning algorithms typically rely on optimization subroutines a...

2 Jesús A. De Loera, et al. ∙

∙ 07/12/2018

An Approximate Message Passing Framework for Side Information

Approximate message passing (AMP) methods have gained recent traction in...

0 Anna Ma, et al. ∙

∙ 05/31/2018

Analysis of Fast Structured Dictionary Learning

Sparsity-based models and techniques have been exploited in many signal ...

0 Saiprasad Ravishankar, et al. ∙

∙ 02/01/2018

Analysis of Fast Alternating Minimization for Structured Dictionary Learning

Methods exploiting sparsity have been popular in imaging and signal proc...

0 Saiprasad Ravishankar, et al. ∙

∙ 01/31/2018

Compressed Anomaly Detection with Multiple Mixed Observations

We consider a collection of independent random variables that are identi...

0 Natalie Durgin, et al. ∙

∙ 11/07/2017

Sparse Randomized Kaczmarz for Support Recovery of Jointly Sparse Corrupted Multiple Measurement Vectors

While single measurement vector (SMV) models have been widely studied in...

0 Natalie Durgin, et al. ∙

∙ 11/05/2017

Stochastic Greedy Algorithms For Multiple Measurement Vectors

Sparse representation of a single measurement vector (SMV) has been expl...

0 Jing Qin, et al. ∙

∙ 05/08/2014

Improving Image Clustering using Sparse Text and the Wisdom of the Crowds

We propose a method to improve image clustering using sparse text and th...

0 Anna Ma, et al. ∙

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