arXiv Machine Learning June 16, 2026 By Laurent Massouli\'e, Sushil Mahavir Varma, Louis Vassaux, Ir\`ene Waldspurger

Phase Transition in Convex Relaxations for Graph Alignment

Read the original on arXiv Machine Learning →

arXiv:2606. 15581v1 Announce Type: cross Abstract: We study the graph alignment problem for correlated Gaussian Orthogonal Ensemble (GOE) matrices, where the goal is to recover a hidden vertex permutation given two correlated symmetric Gaussian matrices $(A, B)$ with correlation $1/\sqrt{1+\sigma^2}$.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

safety

arXiv Machine Learning

Aug 28

Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

arXiv:2608. 26552v1 Announce Type: cross Abstract: Randomized sketch-and-solve algorithms accelerate overconstrained $\ell_2$ regression by replacing the input with a smaller problem.

By Zhao Song, Lichen Zhang

rag

Hugging Face Trending Papers

Aug 27

Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

Randomized sketch-and-solve algorithms accelerate overconstrained $\ell_2$ regression by replacing the input with a smaller problem. Standard subspace embeddings guarantee that the cost of the regress...

rag

arXiv Machine Learning

Sep 7

Centered Permutation Prefixes for SGD with Random Reshuffling: Sharp Rates, H\"older Geometry, and Composite Proximal Extensions

arXiv:2609. 04578v1 Announce Type: cross Abstract: We study stochastic gradient descent with random reshuffling for finite sums \[ F(x)=\frac1n\sum_{i=1}^n f_i(x).

By Jiaxiang Li

arXiv Machine Learning

Jul 17

Spectral Concentration and Recovery in Sparse High-Dimensional Random Geometric Graphs

arXiv:2607. 14304v1 Announce Type: cross Abstract: We study sparse random geometric graphs generated by connecting pairs of high-dimensional vectors whose inner product exceeds a threshold.

By Manuel Fernandez V, Yizhe Zhu

arXiv Statistics ML

Sep 24

Rank-One Signal Recovery in Sparse Wishart Noise

arXiv:2609.28163v1 Announce Type: cross Abstract: We study the high-dimensional recovery of a signal vector $\mathbf{x}$ in the presence of sparse Wishart-like noise. We define an $N \times N$ matrix...

By Preben Forer, Urte Adomaityte, Pierpaolo Vivo

arXiv Machine Learning

Sep 4

Projected Riemannian Gradient Descent for the Bures-Wasserstein Barycenter: Dimension-Independent Linear Convergence at Unit Step Size

arXiv:2609. 03762v1 Announce Type: new Abstract: The computation of the Bures-Wasserstein (BW) barycenter of an ensemble of positive definite matrices arises throughout machine learning, optimal transport, and quantum information.

By A. Afham

One email a morning, machine-written

Related stories

Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

Hadamard Flattening and Gaussian Pooling Sketch for Least Squares with Coordinate-wise Guarantee

Centered Permutation Prefixes for SGD with Random Reshuffling: Sharp Rates, H\"older Geometry, and Composite Proximal Extensions

Spectral Concentration and Recovery in Sparse High-Dimensional Random Geometric Graphs

Rank-One Signal Recovery in Sparse Wishart Noise

Projected Riemannian Gradient Descent for the Bures-Wasserstein Barycenter: Dimension-Independent Linear Convergence at Unit Step Size