collaborators

10 papers

stat.ML2026

Efficient Sequential Evaluation of Large Language Models

Chia-Yu Hsu, Shubhanshu Shekhar

We study the problem of sequentially evaluating a new large language model (LLM) on a fixed question set using historical performance data from prior LLMs. Our goal is to construct…

stat.ML2026

A Semi-Supervised Kernel Two-Sample Test

Gyumin Lee, Shubhanshu Shekhar, Ilmun Kim

We consider the problem of two-sample testing in a semi-supervised setting with abundant unlabeled covariate data. Standard two-sample tests neglect covariate information, which ha…

cs.IT2026

Dual Representation of Minimum Divergence Under Integral Constraints

Shubhanshu Shekhar, Shubhada Agrawal

Minimum divergence problems under integral constraints appear throughout statistics and probability, including sequential inference, bandit theory, and distributionally robust opti…

math.ST2026

Classifier-Based Nonparametric Sequential Hypothesis Testing

Chia-Yu Hsu, Shubhanshu Shekhar

We consider the problem of constructing sequential power-one tests where the null and alternative classes are specified indirectly through historical or offline data. More specific…

math.ST2026

Tighter Confidence Intervals under Without Replacement Sampling via Empirical Rate Functions

Shubhanshu Shekhar, Aaditya Ramdas

We consider the problem of constructing confidence intervals (CIs) for the population mean of values based on a random sample of size , de…

stat.ME2026

Learning to Bet for Horizon-Aware Anytime-Valid Testing

Ege Onur Taga, Samet Oymak, Shubhanshu Shekhar

We develop horizon-aware anytime-valid tests and confidence sequences for bounded means under a strict deadline . Using the betting/e-process framework, we cast horizon-aware be…

cs.LG2026

VINA: Variational Invertible Neural Architectures

Shubhanshu Shekhar, Mohammad Javad Khojasteh, Ananya Acharya +2

The distinctive architectural features of normalizing flows (NFs), notably bijectivity and tractable Jacobians, make them well-suited for generative modeling. Invertible neural net…

eess.SY2026

Outage Identification from Electricity Market Data: Quickest Change Detection Approach

Milad Hoseinpour, Shubhanshu Shekhar, Vladimir Dvorkin

Power system outages expose market participants to significant financial risk unless promptly detected and hedged. We develop an outage identification method from public market sig…

math.ST2025

Active Nonparametric Two-Sample Testing by Betting on Heterogeneous Data Sources

Chia-Yu Hsu, Shubhanshu Shekhar

We study the problem of active nonparametric sequential two-sample testing over multiple heterogeneous data sources. In each time slot, a decision-maker adaptively selects one of $…

math.ST2025

Optimal Anytime-Valid Tests for Composite Nulls

Shubhanshu Shekhar

We consider the problem of designing optimal level- power-one tests for composite nulls. Given a parameter and a stream of -valued observations $\{X_n:…