10 citations · 37 across the 28 of their papers we have counts for
34 papers
Statistical Guarantees for Covariance Estimation with Adversarial Corruption under Constraints
Samuel Ozminkowski, Hongmei Jiang, Matey Neykov
We obtain a near-minimax rate on covariance estimation of adversarially corrupted Gaussian data in Frobenius norm when the covariance estimation is assumed to lie in a star-shaped…
Sparse Convexification for High-Dimensional Constrained Regression
Matey Neykov
We study high-dimensional linear regression under a general symmetric convex constraint. Rather than imposing a specific sparsity-inducing penalty, we start from an arbitrary sign-…
Fast Near-Optimal Estimation over Symmetric Norm Balls
Matey Neykov
This short note proposes a polynomial-time algorithm for near-optimal Euclidean estimation of a signal constrained to lie in the unit ball of a symmetric norm, where the symmetry i…
Efficient Robust Constrained Signal Detection via Kolmogorov Width Approximations
Yikun Li, Matey Neykov
Robust statistical inference often faces a severe computational-statistical gap when dealing with complex parameter spaces. We investigate minimax signal detection in the Gaussian…
Robust mean estimation under star-shaped constraints with heavy-tailed noise
Tuorui Peng, Akshay Prasadan, Matey Neykov
We study the problem of robust mean estimation with adversarially contaminated data under star-shaped constraints in a heavy-tailed noise setting, where only a finite second moment…
Polynomial-Time Near-Optimal Estimation over Certain Type-2 Convex Bodies
Matey Neykov
We develop polynomial-time algorithms for near-optimal minimax mean estimation under -squared loss in a Gaussian sequence model under convex constraints. The parameter spac…