72 citations · 231 across the 61 of their papers we have counts for
10 papers · 1 filter
Statistical Properties of Rectified Flow
Gonzalo Mena, Arun Kumar Kuchibhotla, Larry Wasserman
Rectified flow (Liu et al., 2022; Liu, 2022; Wu et al., 2023) is a method for defining a transport map between two distributions, and enjoys popularity in machine learning, althoug…
Inference for quantile-parametrized families via CDF confidence bands
Srijan Chattopadhyay, Siddhaarth Sarkar, Arun Kumar Kuchibhotla
Quantile-based distribution families are an important subclass of parametric families, capable of exhibiting a wide range of behaviors using very few parameters. These parametric m…
Multiply Robust Conformal Risk Control with Coarsened Data
Manit Paul, Arun Kumar Kuchibhotla, Eric J. Tchetgen Tchetgen
Conformal Prediction (CP) has recently received a tremendous amount of interest, leading to a wide range of new theoretical and methodological results for predictive inference with…
Statistical Inference for Online Algorithms
Selina Carter, Arun K Kuchibhotla
The construction of confidence intervals and hypothesis tests for functionals is a cornerstone of statistical inference. Traditionally, the most efficient procedures - such as the…
Maximal Inequalities for Independent Random Vectors
Supratik Basu, Arun K Kuchibhotla
Maximal inequalities refer to bounds on expected values of the supremum of averages of random variables over a collection. They play a crucial role in the study of non-parametric a…
Tail Bounds for Canonical -Statistics and -Processes with Unbounded Kernels
Abhishek Chakrabortty, Arun K. Kuchibhotla
In this paper, we prove exponential tail bounds for canonical (or degenerate) -statistics and -processes under exponential-type tail assumptions on the kernels. Most of the e…
Finite sample valid confidence sets of mode
Manit Paul, Arun Kumar Kuchibhotla
Estimating the mode of a unimodal distribution is a classical problem in statistics. Although there are several approaches for point-estimation of mode in the literature, very litt…
The Berry-Esseen Bound for High-dimensional Self-normalized Sums
Woonyoung Chang, Kenta Takatsu, Konrad Urban +1
This manuscript studies the Gaussian approximation of the coordinate-wise maximum of self-normalized statistics in high-dimensional settings. We derive an explicit Berry-Esseen bou…
Honest Inference for Stochastic Optimization
Kenta Takatsu, Arun Kumar Kuchibhotla
This manuscript studies a general approach to construct confidence sets for the solution of stochastic optimization, rendering empirical risk minimization as special cases. Statist…
Doubly Robust and Efficient Calibration of Prediction Sets for Right-Censored Time-to-Event Outcomes
Rebecca Farina, Eric J. Tchetgen Tchetgen, Arun Kumar Kuchibhotla
Our objective is to construct well-calibrated prediction sets for a time-to-event outcome subject to right-censoring with guaranteed coverage. Inspired by modern conformal inferenc…