3 papers
cs.LG2026
Conformal Changepoint Localization and Root Cause Analysis with Corrupted Observations
Seunghun Yu, Meiyi Zhu, Petar Popovski +2
Detecting when the statistical behavior of an engineered system changes, and identifying which component is responsible, are core problems in the monitoring of telecommunication ne…
cs.LG2025
CG-FKAN: Compressed-Grid Federated Kolmogorov-Arnold Networks for Communication Constrained Environment
Seunghun Yu, Youngjoon Lee, Jinu Gong +1
Federated learning (FL), widely used in privacy-critical applications, suffers from limited interpretability, whereas Kolmogorov-Arnold Networks (KAN) address this limitation via l…
cs.LG2025
FedEFC: Federated Learning Using Enhanced Forward Correction Against Noisy Labels
Seunghun Yu, Jin-Hyun Ahn, Joonhyuk Kang
Federated Learning (FL) is a powerful framework for privacy-preserving distributed learning. It enables multiple clients to collaboratively train a global model without sharing raw…