12 citations · 24 across the 18 of their papers we have counts for
4 papers · 1 filter
PAL: Pluralistic Alignment Framework for Learning from Heterogeneous Preferences
Daiwei Chen, Yi Chen, Aniket Rege +1
Large foundation models pretrained on raw web-scale data are not readily deployable without additional step of extensive alignment to human preferences. Such alignment is typically…
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…
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…
Metric Learning from Limited Pairwise Preference Comparisons
Zhi Wang, Geelon So, Ramya Korlakai Vinayak
We study metric learning from preference comparisons under the ideal point model, in which a user prefers an item over another if it is closer to their latent ideal item. These ite…