4 citations · 5 across the 6 of their papers we have counts for
9 papers · 1 filter
Mitigating multiple descents: A model-agnostic framework for risk monotonization
Pratik Patil, Arun Kumar Kuchibhotla, Yuting Wei +1
Recent empirical and theoretical analyses of several commonly used prediction procedures reveal a peculiar risk behavior in high dimensions, referred to as double/multiple descent,…
Median bias of M-estimators
Arun Kumar Kuchibhotla
In this note, we derive bounds on the median bias of univariate M-estimators under mild regularity conditions. These requirements are not sufficient to imply convergence in distrib…
High-dimensional CLT for Sums of Non-degenerate Random Vectors: -rate
Arun Kumar Kuchibhotla, Alessandro Rinaldo
In this note, we provide a Berry--Esseen bounds for rectangles in high-dimensions when the random vectors have non-singular covariance matrices. Under this assumption of non-singul…
All of Linear Regression
Arun K. Kuchibhotla, Lawrence D. Brown, Andreas Buja +1
Least squares linear regression is one of the oldest and widely used data analysis tools. Although the theoretical analysis of the ordinary least squares (OLS) estimator is as old,…
First order expansion of convex regularized estimators
Pierre C Bellec, Arun K Kuchibhotla
We consider first order expansions of convex penalized estimators in high-dimensional regression problems with random designs. Our setting includes linear regression and logistic r…
On Least Squares Estimation under Heteroscedastic and Heavy-Tailed Errors
Arun K. Kuchibhotla, Rohit K. Patra
We consider least squares estimation in a general nonparametric regression model. The rate of convergence of the least squares estimator (LSE) for the unknown regression function i…