5 papers · 1 filter
Estimation beyond Missing (Completely) at Random
Tianyi Ma, Kabir A. Verchand, Thomas B. Berrett +2
We study the effects of missingness on the estimation of population parameters. Moving beyond restrictive missing completely at random (MCAR) assumptions, we first formulate a miss…
High-dimensional estimation with missing data: Statistical and computational limits
Kabir Aladin Verchand, Ankit Pensia, Saminul Haque +1
We consider computationally-efficient estimation of population parameters when observations are subject to missing data. In particular, we consider estimation under the realizable…
State evolution beyond first-order methods I: Rigorous predictions and finite-sample guarantees
Michael Celentano, Chen Cheng, Ashwin Pananjady +1
We develop a toolbox for exact analysis of iterative algorithms on a class of high-dimensional nonconvex optimization problems with random data. While prior work has shown that low…
High-dimensional logistic regression with missing data: Imputation, regularization, and universality
Kabir Aladin Verchand, Andrea Montanari
We study high-dimensional, ridge-regularized logistic regression in a setting in which the covariates may be missing or corrupted by additive noise. When both the covariates and th…
High-probability minimax lower bounds
Tianyi Ma, Kabir A. Verchand, Richard J. Samworth
The minimax risk is often considered as a gold standard against which we can compare specific statistical procedures. Nevertheless, as has been observed recently in robust and heav…