11 papers
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…
Robust Signal Detection with Quadratically Convex Orthosymmetric Constraints
Yikun Li, Matey Neykov
This paper studies the problem of robust signal detection in Gaussian noise under quadratically convex orthosymmetric (QCO) constraints. We consider a minimax testing framework whe…