Search Results for author: Kiryung Lee

Found 8 papers, 1 papers with code

Randomly Initialized Alternating Least Squares: Fast Convergence for Matrix Sensing

no code implementations25 Apr 2022 Kiryung Lee, Dominik Stöger

In this paper, we show that ALS with random initialization converges to the true solution with $\varepsilon$-accuracy in $O(\log n + \log (1/\varepsilon)) $ iterations using only a near-optimal amount of samples, where we assume the measurement matrices to be i. i. d.

Max-Linear Regression by Scalable and Guaranteed Convex Programming

no code implementations12 Mar 2021 Seonho Kim, Sohail Bahmani, Kiryung Lee

When the $k$ linear components are equally likely to achieve the maximum, our result shows that a sufficient number of observations scales as $k^{2}p$ up to a logarithmic factor.

Low-Rank Matrix Estimation From Rank-One Projections by Unlifted Convex Optimization

no code implementations6 Apr 2020 Sohail Bahmani, Kiryung Lee

We study an estimator with a convex formulation for recovery of low-rank matrices from rank-one projections.

Convolutional Framework for Accelerated Magnetic Resonance Imaging

1 code implementation8 Feb 2020 Shen Zhao, Lee C. Potter, Kiryung Lee, Rizwan Ahmad

Magnetic Resonance Imaging (MRI) is a noninvasive imaging technique that provides exquisite soft-tissue contrast without using ionizing radiation.

Image Reconstruction

Decentralized sketching of low rank matrices

no code implementations NeurIPS 2019 Rakshith Sharma Srinivasa, Kiryung Lee, Marius Junge, Justin Romberg

We address a low-rank matrix recovery problem where each column of a rank-r matrix X of size (d1, d2) is compressed beyond the point of recovery to size L with L << d1.

Blind Gain and Phase Calibration via Sparse Spectral Methods

no code implementations30 Nov 2017 Yanjun Li, Kiryung Lee, Yoram Bresler

We also show that our power iteration algorithms for BGPC compare favorably with competing algorithms in adversarial conditions, e. g., with noisy measurement or with a bad initial estimate.

Generalized notions of sparsity and restricted isometry property. Part I: A unified framework

no code implementations28 Jun 2017 Marius Junge, Kiryung Lee

The restricted isometry property (RIP) is an integral tool in the analysis of various inverse problems with sparsity models.

Dimensionality Reduction

Generalized notions of sparsity and restricted isometry property. Part II: Applications

no code implementations28 Jun 2017 Marius Junge, Kiryung Lee

The restricted isometry property (RIP) is a universal tool for data recovery.

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