arXiv:2606. 14335v1 Announce Type: cross Abstract: Recovering structural information from noisy high-dimensional data is a fundamental task in statistical inference.
By Zhe Hou, Jingcheng Liu
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:2607. 17469v1 Announce Type: cross Abstract: A randomized algorithm may terminate almost surely even though exceptional random tapes make it run forever.
By Yunbei Xu
arXiv:2606. 02055v1 Announce Type: cross Abstract: We study exact community recovery in the two-community stochastic block model on $n$ vertices under limited and noisy access to network data.
By Sabyasachi Basu, Manuj Mukherjee, Lutz Oettershagen, Suhas Thejaswi
arXiv:2607. 16676v1 Announce Type: cross Abstract: How deep does a graph neural network need to be on a sparse graph?
By Aseem Raj Baranwal
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)