From the 1 of 16 linked papers with an AI index.
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Lehner's operator norm formulas, semidefinite programming, and spiked matrix models
Dmitriy Kunisky
Lehner (1999) derived elegant formulas for the operator norm of operators of the form $\mathfrak{X} = \mathbf{A}_0 \otimes \mathfrak{1} + \sum_{i = 1}^n \mathbf{…
A revision of Litvak's conjecture on Gaussian minima and a volumetric zone conjecture
Dmitriy Kunisky
Litvak (2018) conjectured that, for any , the quantity where is a centered Gaussian random vector is minimiz…
Universality of first-order methods on random and deterministic matrices
Nicola Gorini, Chris Jones, Dmitriy Kunisky +1
General first-order methods (GFOM) are a flexible class of iterative algorithms which update a state vector by matrix-vector multiplications and entrywise nonlinearities. A long li…
Gurau's spectral density is not a probability measure for individual real symmetric tensors
Maximilian Jerdee, Dmitriy Kunisky, Cristopher Moore
Gurau (2020) proposed a generalization of the trace of the matrix resolvent to tensors of higher order, and recent work has explored analogs of the Wigner semicircle and Marchenko-…
Generalized noise sensitivity of eigenvectors: All eigenvectors, inhomogeneous variance profiles, and dependent resampling
Xiangyi Zhu, Dmitriy Kunisky
Chatterjee (2016) proved, as an application of his general framework relating superconcentration and chaos, that after the entries of an matrix drawn from the Gaussian…
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…