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Marcus Hutter (DeepMind and Australian National University): Learning Curve Theory

Ort: MPI für Mathematik in den Naturwissenschaften Leipzig,  , Videobroadcast

Video broadcast: Math Machine Learning seminar MPI MIS + UCLA Recently a number of empirical "universal" scaling law papers have been published, most notably by OpenAI. `Scaling laws' refers to power-law decreases of training or test error w.r.t. more data, larger neural networks, and/or more compute. In this work we focus on scaling w.r.t. data size n. Theoretical understanding of this phenomenon is largely lacking, except in finite-dimensional models for which error typically decreases with n^−1/2 or n^−1, where n is the sample size. We develop and theoretically analyse the simplest possible (toy) model that can exhibit n^−β learning curves for arbitrary power β>0, and determine whether power laws are universal or depend on the data distribution.

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Beginn: 4. März 2021 17:00

Ende: 4. März 2021 18:30