4 papers
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…
Testing Calibration in Nearly-Linear Time
Lunjia Hu, Arun Jambulapati, Kevin Tian +1
In the recent literature on machine learning and decision making, calibration has emerged as a desirable and widely-studied statistical property of the outputs of binary prediction…
On Computationally Efficient Multi-Class Calibration
Parikshit Gopalan, Lunjia Hu, Guy N. Rothblum
Consider a multi-class labelling problem, where the labels can take values in , and a predictor predicts a distribution over the labels. In this work, we study the following f…
Multigroup Robustness
Lunjia Hu, Charlotte Peale, Judy Hanwen Shen
To address the shortcomings of real-world datasets, robust learning algorithms have been designed to overcome arbitrary and indiscriminate data corruption. However, practical proce…