no code implementations • 25 Apr 2023 • Jin-Hong Du, Pratik Patil, Arun Kumar Kuchibhotla
We study subsampling-based ridge ensembles in the proportional asymptotics regime, where the feature size grows proportionally with the sample size such that their ratio converges to a constant.
no code implementations • 12 Apr 2023 • Siddhaarth Sarkar, Arun Kumar Kuchibhotla
Prediction sets with both classes being undesirable, the analyst might desire to consider, say 80$\%$ prediction set.
no code implementations • 27 Feb 2023 • Jin-Hong Du, Pratik Patil, Kathryn Roeder, Arun Kumar Kuchibhotla
By establishing uniform consistency of our risk extrapolation technique over ensemble and subsample sizes, we show that ECV yields $\delta$-optimal (with respect to the oracle-tuned risk) ensembles for squared prediction risk.
no code implementations • 20 Oct 2022 • Pratik Patil, Jin-Hong Du, Arun Kumar Kuchibhotla
Bagging is a commonly used ensemble technique in statistics and machine learning to improve the performance of prediction procedures.
no code implementations • 25 May 2022 • Pratik Patil, Arun Kumar Kuchibhotla, Yuting Wei, Alessandro Rinaldo
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, in which the asymptotic risk is a non-monotonic function of the limiting aspect ratio of the number of features or parameters to the sample size.
no code implementations • 26 Aug 2020 • Richard A. Berk, Arun Kumar Kuchibhotla
Risk assessment algorithms have been correctly criticized for potential unfairness, and there is an active cottage industry trying to make repairs.
1 code implementation • 9 Jun 2020 • Arun Kumar Kuchibhotla, Qinqing Zheng
Many inference problems, such as sequential decision problems like A/B testing, adaptive sampling schemes like bandit selection, are often online in nature.
no code implementations • 13 May 2020 • Arun Kumar Kuchibhotla
The concept of exchangeability is also at the core of rank tests widely known in nonparametric statistics.
no code implementations • 13 Sep 2018 • Arun Kumar Kuchibhotla
Ever since the proof of asymptotic normality of maximum likelihood estimator by Cramer (1946), it has been understood that a basic technique of the Taylor series expansion suffices for asymptotics of $M$-estimators with smooth/differentiable loss function.
no code implementations • 8 Apr 2018 • Arun Kumar Kuchibhotla, Abhishek Chakrabortty
The third example concerns the restricted eigenvalue condition, required in HD linear regression, which we verify for all sub-Weibull random vectors through a unified analysis, and also prove a more general result related to restricted strong convexity in the process.