Retrieving, analyzing, and processing large data can be challenging. An effective and efficient mechanism for overcoming these challenges is to cluster the data into a compact …
E Abbe, J Fan, K Wang, Y Zhong - Annals of statistics, 2020 - ncbi.nlm.nih.gov
Recovering low-rank structures via eigenvector perturbation analysis is a common problem in statistical machine learning, such as in factor analysis, community detection, ranking …
C Gao, Z Ma, AY Zhang, HH Zhou - Journal of Machine Learning Research, 2017 - jmlr.org
Community detection is a fundamental statistical problem in network data analysis. In this paper, we present a polynomial time two-stage method that provably achieves optimal …
B Hajek, Y Wu, J Xu - IEEE Transactions on Information Theory, 2016 - ieeexplore.ieee.org
The binary symmetric stochastic block model deals with a random graph of n vertices partitioned into two equal-sized clusters, such that each pair of vertices is independently …
Supplement to “Mimimax rates of community detection in stochastic block models”. In the supplement 31, we provide proofs of Lemma 5.2, Propositions 5.1 and 5.2. We also provide …
M Brennan, G Bresler - Conference on Learning Theory, 2020 - proceedings.mlr.press
Inference problems with conjectured statistical-computational gaps are ubiquitous throughout modern statistics, computer science, statistical physics and discrete probability …
C Moore - arXiv preprint arXiv:1702.00467, 2017 - arxiv.org
Community detection in graphs is the problem of finding groups of vertices which are more densely connected than they are to the rest of the graph. This problem has a long history, but …