works on

From the 1 of 20 linked papers with an AI index.

activity
20242026
most citedRethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Data

1 citations · 1 across the 7 of their papers we have counts for

collaborators

20 papers

cs.LG2026

Active Exploration via Autoregressive Generation of Missing Data

Tiffany Tianhui Cai, Hongseok Namkoong, Daniel Russo +1

The paper proposes using autoregressive sequence models to quantify uncertainty and guide exploration in online decision-making, showing that Bayesian regret can be bounded by offl…

math.OC2026

Design and Scheduling of an AI-based Queueing System

Jiung Lee, Hongseok Namkoong, Yibo Zeng

To leverage prediction models to make optimal scheduling decisions in service systems, we must understand how predictive errors impact congestion due to externalities on the delay…

stat.ME2026

When Representative Samples Produce Worse Outcomes: Scale-up Decisions and Testing in Small-Budget RCTs

Hannah Li, Hongseok Namkoong, Isaac Scheinfeld

Small randomized controlled trials are often used to screen interventions before running larger follow-up studies. This is a critical phase of experimentation, as missing effective…

cs.LG20261 cited

Rethinking Distribution Shifts: Empirical Analysis and Modeling for Tabular Data

Tianyu Wang, Jiashuo Liu, Peng Cui +1

Different distribution shifts require different interventions, and algorithms must be grounded in the specific shifts they address. However, methodological development for robust a…

math.ST2026

Local Sensitivity Under Transport Restrictions

Hongseok Namkoong

We quantify the value of structural knowledge, restrictions a modeler places on the world before seeing data. Our analytic workhorse is the local sensitivity of an estimand to dist…

stat.ME2026

Empirical Likelihood for Nonsmooth Functionals

Hongseok Namkoong

Empirical likelihood is an attractive inferential framework that respects natural parameter boundaries, but existing approaches typically require smoothness of the functional and m…