3 papers
cs.LG2025
ScriptoriumWS: A Code Generation Assistant for Weak Supervision
Tzu-Heng Huang, Catherine Cao, Spencer Schoenberg +3
Weak supervision is a popular framework for overcoming the labeled data bottleneck: the need to obtain labels for training data. In weak supervision, multiple noisy-but-cheap sourc…
cs.LG2024
Taming False Positives in Out-of-Distribution Detection with Human Feedback
Harit Vishwakarma, Heguang Lin, Ramya Korlakai Vinayak
Robustness to out-of-distribution (OOD) samples is crucial for safely deploying machine learning models in the open world. Recent works have focused on designing scoring functions…
cs.LG2024
Pearls from Pebbles: Improved Confidence Functions for Auto-labeling
Harit Vishwakarma, Reid, Chen +4
Auto-labeling is an important family of techniques that produce labeled training sets with minimum manual labeling. A prominent variant, threshold-based auto-labeling (TBAL), works…