arXiv Machine Learning June 9, 2026 By Pierre Agui\'e, Mathieu Even, Laurent Massouli\'e

Improved Analysis of the Accelerated Noisy Power Method with Applications to Decentralized PCA

Read the original on arXiv Machine Learning →

arXiv:2602. 03682v2 Announce Type: replace-cross Abstract: We analyze the Accelerated Noisy Power Method, an algorithm for Principal Component Analysis in the setting where only inexact matrix-vector products are available, which can arise for instance in decentralized PCA.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

arXiv Machine Learning

Jun 2

Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation

arXiv:2505. 10882v2 Announce Type: replace Abstract: Principal component analysis classically requires full $d$-dimensional samples, yet in various applications hardware limits acquisition to a few scalar measurements per sample.

By Alex Saad-Falcon, Brighton Ancelin, Justin Romberg

arXiv Machine Learning

Jun 2

Easy, robust approximate message passing for planted spike models

arXiv:2606. 00500v1 Announce Type: cross Abstract: We present a simple and efficient algorithm for robust approximate message passing (AMP) in the spiked matrix setting.

By Misha Ivkov, Tselil Schramm

arXiv AI

Jun 3

DeMuon: A Decentralized Muon for Matrix Optimization over Graphs

arXiv:2510. 01377v2 Announce Type: replace-cross Abstract: In this paper, we propose DeMuon, a method for decentralized matrix optimization over a given communication topology.

By Chuan He, Shuyi Ren, Jingwei Mao, Erik G. Larsson

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arXiv Machine Learning

Aug 11

Sparse corruption in low-rank matrix inference: the PCA benchmark

arXiv:2511. 11927v2 Announce Type: replace-cross Abstract: Principal Component Analysis (PCA) is a standard tool for extracting a low-rank signal from noisy observations.

By Urte Adomaityte, Gabriele Sicuro, Pierpaolo Vivo

benchmarks safety

arXiv Machine Learning

Aug 11

Understanding Alternating Minimization for Matrix Completion

arXiv:1312. 0925v4 Announce Type: replace Abstract: Alternating Minimization is a widely used and empirically successful heuristic for matrix completion and related low-rank optimization problems.

By Moritz Hardt

arXiv Machine Learning

Jul 21

Exploiting Low-Rank Objective Structure in Discrete Quadratic Optimization

arXiv:2602. 20376v3 Announce Type: replace-cross Abstract: We study the problem of maximizing a complex-valued quadratic form over the $K^{\text{th}}$ roots of unity.

By Ria Stevens, Fangshuo Liao, Barbara Su, Thanasis Hadjidimoulas, Jianqiang Li, Anastasios Kyrillidis

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Global Convergence of Adaptive Sensing for Principal Eigenvector Estimation

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