arXiv Machine Learning

Phase Transition for Stochastic Block Model with more than $\sqrt{n}$ Communities

arXiv:2509. 15822v3 Announce Type: replace-cross Abstract: Predictions from statistical physics postulate that recovery of the communities in the Stochastic Block Model (SBM) with a fixed number $K$ of communities is possible in polynomial time above, and only above, the Kesten-Stigum (KS) threshold.

arXiv Machine Learning
Jul 3

Provably Finding a Hidden Dense Submatrix among Many Planted Dense Submatrices via Convex Programming

arXiv:2601. 03946v3 Announce Type: replace-cross Abstract: We consider the densest submatrix problem, which seeks the submatrix of fixed size of a given binary matrix that contains the most nonzero entries.

By Valentine Olanubi (University of Alabama, Department of Mathematics), Phineas Agar (University of Alabama, Department of Mathematics), Brendan Ames (University of Southampton, School of Mathematical Sciences)
arXiv Machine Learning
Jun 5

Sharp Low-Degree Thresholds for Planted-vs-Planted Testing

arXiv:2606. 05266v1 Announce Type: new Abstract: We establish the first sharp thresholds for low-degree polynomial tests in planted-vs-planted settings, where the goal is to determine with vanishing error which of two structured planted mechanisms generated the observed data.

By Anda Skeja, Daniel Guti\'errez Espinoza, Fiona Skerman, Alexander S. Wein
arXiv Machine Learning
Jul 10

High-Dimensional Procrustes Matching via Tree Counts

arXiv:2607. 08538v1 Announce Type: cross Abstract: Suppose we observe two sets of $n$ Gaussian vectors in $\mathbb{R}^d$, with the promise that, after applying a permutation of $[n]$ and a rotation of $\mathbb{R}^d$, the two sets are $\rho$-correlated.

By Xiaochun Niu, Tselil Schramm, Jiaming Xu