From the 1 of 24 linked papers with an AI index.
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Universal entrywise eigenvector fluctuations in delocalized spiked matrix models and asymptotics of rounded spectral algorithms
Shujing Chen, Dmitriy Kunisky
We consider the distribution of the top eigenvector of a spiked matrix model of the form , in the supercritical regime where has an outlier eigenva…
Nonlinear Laplacians: Tunable principal component analysis under directional prior information
Yuxin Ma, Dmitriy Kunisky
We introduce a new family of algorithms for detecting and estimating a rank-one signal from a noisy observation under prior information about that signal's direction, focusing on e…
Computational and statistical lower bounds for low-rank estimation under general inhomogeneous noise
Debsurya De, Dmitriy Kunisky
Recent work has generalized several results concerning the well-understood spiked Wigner matrix model of a low-rank signal matrix corrupted by additive i.i.d. Gaussian noise to the…
Asymptotic Bounds and Online Algorithms for Average-Case Matrix Discrepancy
Dmitriy Kunisky, Timm Oertel, Nicola Wengiel +1
We study the matrix discrepancy problem in the average-case setting. Given a sequence of symmetric matrices , its discrepancy is defined as the minimal…
The Lovász number of random circulant graphs
Afonso S. Bandeira, JarosÅaw BÅasiok, Daniil Dmitriev +3
This paper addresses the behavior of the Lovász number for dense random circulant graphs. The Lovász number is a well-known semidefinite programming upper bound on the independen…
On the Structure of Bad Science Matrices
Alex Albors, Hisham Bhatti, Lukshya Ganjoo +5
The bad science matrix problem consists in finding, among all matrices with rows having unit norm, one that maximizes $β(A) = \frac{1}{2^n…