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Heguang Lin

2 accepted papers

2024

Taming False Positives in Out-of-Distribution Detection with Human Feedback

AISTATS 2024poster

Robustness to out-of-distribution (OOD) samples is crucial for the safe deployment of machine learning models in the open world. Recent works have focused on designing scoring functions to quantify OOD uncertainty. Setting appropriate thresholds for these scoring functions for OOD detection is chall…

2023

Promises and Pitfalls of Threshold-based Auto-labeling

NeurIPS 2023spotlight

Creating large-scale high-quality labeled datasets is a major bottleneck in supervised machine learning workflows. Threshold-based auto-labeling (TBAL), where validation data obtained from humans is used to find a confidence threshold above which the data is machine-labeled, reduces reliance on manu…