Jungbin Jun

I am a Statistics undergraduate at Inha University, working under the supervision of Prof. Ilsang Ohn in the Theoretical Statistics Lab.

My research interests lie in reliable learning under uncertainty and distribution shift, drawing on both frequentist and Bayesian perspectives. In particular, I am interested in conformal prediction, generalized Bayesian inference, and PAC-Bayesian theory.

Email  /  CV  /  Github

Profile photo

Education
Inha University
B.S. in Statistics
Mar 2022 - Sep 2026 (Expected)
  • GPA: 4.42 / 4.50 (Major: 4.47 / 4.50)
  • Early Graduation (3 years)
  • Rank 1/41 in the department

Publications
Online Conformal Inference with Retrospective Adjustment for Faster Adaptation to Distribution Shift
Jungbin Jun, Ilsang Ohn
arXiv preprint, 2025
paper / code

RetroAdj is an online conformal method that retrospectively adjusts prediction intervals to improve adaptation under distribution shift while maintaining long-run coverage.

Early-stopped Aggregation: Adaptive Inference with Computational Efficiency
Ilsang Ohn, Shitao Fan, Jungbin Jun, Lizhen Lin
arXiv preprint, 2026
paper

ESA is a framework for efficient adaptive aggregation that applies to both Bayesian and frequentist inference, reducing unnecessary computation while preserving the benefits of adaptive aggregation.

Website template adapted from Jon Barron.