Chutong Yang

Achieving optimal recalibration rates in online prediction could redefine performance benchmarks in adaptive learning systems.

Forget single-objective optimization—this work cracks omniprediction in multiclass settings, opening the door to algorithms that are robust across diverse loss functions and comparator classes.