1 citations · 1 across the 9 of their papers we have counts for
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Near-optimal Swap Regret Minimization for Convex Losses
Lunjia Hu, Jon Schneider, Yifan Wu
We give a randomized online algorithm that guarantees near-optimal expected swap regret against any sequence of adaptively chosen Lipschitz convex losse…
Coherence Mechanisms for Provable Self-Improvement
Mehryar Mohri, Jon Schneider, Yifan Wu
Self-improvement is a critical capability for large language models and other intelligent systems, enabling them to refine their behavior and internal consistency without external…
Truthful Calibration Errors for Multi-Class Prediction
Yuxuan Lu, Yifan Wu, Jason Hartline +1
Calibrated predictions are useful because their numerical values can be interpreted as probabilities. Calibration errors are therefore widely used to evaluate, compare, and tune pr…
A Perfectly Truthful Calibration Measure
Jason Hartline, Lunjia Hu, Yifan Wu
Calibration requires that predictions are conditionally unbiased and, therefore, reliably interpretable as probabilities. A calibration measure quantifies how far a predictor is fr…
Calibration Error for Decision Making
Lunjia Hu, Yifan Wu
Calibration allows predictions to be reliably interpreted as probabilities by decision makers. We propose a decision-theoretic calibration error, the Calibration Decision Loss (CDL…