Matey Neykov
Carnegie Mellon University
34 Papers
212 Citations
Matey Neykov is an academic researcher from Carnegie Mellon University. The author has contributed to research in topics: Graph (abstract data type) & Minimax. The author has an hindex of 10, co-authored 33 publications. Previous affiliations of Matey Neykov include Princeton University & Harvard University.
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Papers
A Unified Theory of Confidence Regions and Testing for High-Dimensional Estimating Equations
TL;DR: In this paper, a new inferential framework for constructing confidence regions and testing hypotheses in statistical models specified by a system of high-dimensional estimating equations is proposed, which constructs an influence function by projecting the fitted estimating equations to a sparse direction obtained by solving a large-scale linear program.
•Posted Content
A Unified Theory of Confidence Regions and Testing for High Dimensional Estimating Equations
TL;DR: A new inferential framework for constructing confidence regions and testing hypotheses in statistical models specified by a system of high dimensional estimating equations is proposed, which is likelihood-free and provides valid inference for a broad class of highdimensional constrained estimating equation problems, which are not covered by existing methods.
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•Journal Article
L 1 -regularized least squares for support recovery of high dimensional single index models with Gaussian designs
Matey Neykov,Jun Liu,Tianxi Cai +2 more
TL;DR: Algorithms based on covariance screening and least squares with L1 penalization and LASSO can also enjoy optimal (up to a scalar) rescaled sample size in terms of support recovery, albeit under slightly different assumptions on f and ε compared to the SIR based algorithms.
Property testing in high-dimensional ising models
Matey Neykov,Han Liu +1 more
TL;DR: This paper explores the information-theoretic limitations of graph property testing in zero-field Ising models, and proposes two types of correlation based tests: computationally efficient screening for ferromagnets, and score type tests for general models, including a fast cycle presence test.
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Misspecified nonconvex statistical optimization for sparse phase retrieval
TL;DR: A simple variant of the thresholded Wirtinger flow algorithm is proposed that, given a proper initialization, linearly converges to an estimator with optimal statistical accuracy for a broad family of unknown link functions.
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