3 citations · 3 across the 5 of their papers we have counts for
3 papers · 1 filter
SAUL: Sharpness-Aware Augmented-Lagrangian Unlearning
Jaewan Choi, Junyoung Yang, Sangdon Park
Machine unlearning in Large Language Models (LLMs) faces a critical trade-off between erasing target knowledge and preserving general utility. We propose SAUL (Sharpness-Aware Augm…
Online Conformal Prediction with Adversarial Semi-bandit Feedback via Regret Minimization
Junyoung Yang, Kyungmin Kim, Sangdon Park
Uncertainty quantification is crucial in safety-critical systems, where decisions must be made under uncertainty. In particular, we consider the problem of online uncertainty quant…
CODiT: Conformal Out-of-Distribution Detection in Time-Series Data
Ramneet Kaur, Kaustubh Sridhar, Sangdon Park +4
Machine learning models are prone to making incorrect predictions on inputs that are far from the training distribution. This hinders their deployment in safety-critical applicatio…