Still using temperature scaling? With @dholzmueller.bsky.social, Michael I. Jordan and @bachfrancis.bsky.social we argue that with well designed regularization, more expressive models like matrix scaling can outperform simpler ones across calibration set sizes, data dimensions, and applications.

COLT Workshop on Predictions and Uncertainty was a banger! I was lucky to present our paper "Minimum Volume Conformal Sets for Multivariate Regression", alongside my colleague @eberta.bsky.social and his awsome work on calibration. Big thanks to the organizers! #ConformalPrediction #MarcoPolo

What if we have been doing early stopping wrong all along? When you break the validation loss into two terms, calibration and refinement you can make the simplest (efficient) trick to stop training in a smarter position

Early stopping on validation loss? This leads to suboptimal calibration and refinement errors—but you can do better! With @dholzmueller.bsky.social, Michael I. Jordan, and @bachfrancis.bsky.social, we propose a method that integrates with any model and boosts classification performance across tasks.