8 citations · 9 across the 3 of their papers we have counts for
6 papers · 1 filter
Label Noise Robustness of Conformal Prediction
Bat-Sheva Einbinder, Shai Feldman, Stephen Bates +3
We study the robustness of conformal prediction, a powerful tool for uncertainty quantification, to label noise. Our analysis tackles both regression and classification problems, c…
How to Evaluate Reward Models for RLHF
Evan Frick, Tianle Li, Connor Chen +6
We introduce a new benchmark for reward models that quantifies their ability to produce strong language models through RLHF (Reinforcement Learning from Human Feedback). The gold-s…
Online conformal prediction with decaying step sizes
Anastasios N. Angelopoulos, Rina Foygel Barber, Stephen Bates
We introduce a method for online conformal prediction with decaying step sizes. Like previous methods, ours possesses a retrospective guarantee of coverage for arbitrary sequences.…
Conformal Decision Theory: Safe Autonomous Decisions from Imperfect Predictions
Jordan Lekeufack, Anastasios N. Angelopoulos, Andrea Bajcsy +2
We introduce Conformal Decision Theory, a framework for producing safe autonomous decisions despite imperfect machine learning predictions. Examples of such decisions are ubiquitou…
Data-Adaptive Tradeoffs among Multiple Risks in Distribution-Free Prediction
Drew T. Nguyen, Reese Pathak, Anastasios N. Angelopoulos +2
Decision-making pipelines are generally characterized by tradeoffs among various risk functions. It is often desirable to manage such tradeoffs in a data-adaptive manner. As we dem…
PPI++: Efficient Prediction-Powered Inference
Anastasios N. Angelopoulos, John C. Duchi, Tijana Zrnic
We present PPI++: a computationally lightweight methodology for estimation and inference based on a small labeled dataset and a typically much larger dataset of machine-learning pr…