3 papers
cs.LG2026
Pushing the (Decision) Boundaries: Dynamically Calibrating Differentially Private Noise to Explainability in Federated Learning
Michael Khavkin, Kichang Lee, Jaeho Jin +2
Federated Learning (FL) with Differential Privacy (DP) is increasingly adopted to preserve data confidentiality in distributed machine learning. However, DP noise distorts learned…
cs.IR2026
Unified Pitch Graphs for Diagnosing Pitching Strategy
Kichang Lee, JeongGil Ko
Pitching strategy in baseball is expressed through both physical execution and the ordered context in which pitches are used, yet common representations collapse pitches into discr…
cs.HC2026
Auditing Contextual Bias in Human Ball-Strike Calls Using KBO's Automated Umpiring Transition
Kichang Lee, JeongGil Ko
This paper uses the Korean Baseball Organization's adoption of the Automated Ball-Strike (ABS) system to audit long-standing claims about contextual bias in human ball-strike calls…