3 citations · 3 across the 5 of their papers we have counts for
5 papers
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…
Uncertainty Quantification for Neurosymbolic Programs via Compositional Conformal Prediction
Ramya Ramalingam, Sangdon Park, Osbert Bastani
Machine learning has become an effective tool for automatically annotating unstructured data (e.g., images) with structured labels (e.g., object detections). As a result, a new pro…
PAC Prediction Sets Under Label Shift
Wenwen Si, Sangdon Park, Insup Lee +2
Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes.…
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…