Beyond the Loss Curve: Scaling Laws, Active Learning, and the Limits of Learning from Exact Posteriors

Abstract

Class-conditional normalizing flows provide tractable posterior probabilities for studying learning behavior. This framework examines scaling laws, soft-label training, distribution shifts, and active learning while separating epistemic error from the uncertainty that remains even for an optimal predictor.

Ravid Shwartz-Ziv

AI Researcher

AI researcher at Meta MSL with a background in information theory and computational neuroscience, working on world models, memory, and compression. Former Assistant Professor and Faculty Fellow at NYU, collaborating on research across academia and industry.