Hyperparameter Loss Surfaces Are Simple Near their Optima

Nicholas Lourie, He He, Kyunghyun Cho

Keywords: experimental design, hyperparameters, scaling laws

TL;DR: We derive a theory describing the hyperparameter loss surface and yielding new statistical tools for understanding it.

Abstract: Hyperparameters greatly impact models' capabilities; however, modern models are too large for extensive search. Instead, researchers design recipes that train well across scales based on their understanding of the hyperparameters. Despite this importance, few tools exist for understanding the hyperparameter loss surface. We discover novel structure in it and propose a new theory yielding such tools. The loss surface is complex, but as you approach the optimum simple structure emerges. It becomes characterized by a few basic features, like its effective dimension and the best possible loss. To uncover this *asymptotic regime*, we develop a novel technique based on random search. Within this regime, the best scores from random search take on a new distribution we discover. Its parameters are exactly the features defining the loss surface in the asymptotic regime. From these features, we derive a new asymptotic law for random search that can explain and extrapolate its convergence. These new tools enable new analyses, such as confidence intervals for the best possible performance or determining the effective number of hyperparameters. We make these tools available at: https://github.com/nicholaslourie/opda.

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Submission Number: 1841