Stochastic Hyperparameter Optimization through Hypernetworks

Jonathan Lorraine, David Duvenaud

Keywords: hypernetworks, hyperparameter optimization, metalearning, neural networks, Bayesian optimization, game theory, optimization

TL;DR: We train a neural network to output approximately optimal weights as a function of hyperparameters.

Abstract: Machine learning models are often tuned by nesting optimization of model weights inside the optimization of hyperparameters. We give a method to collapse this nested optimization into joint stochastic optimization of weights and hyperparameters. Our process trains a neural network to output approximately optimal weights as a function of hyperparameters. We show that our technique converges to locally optimal weights and hyperparameters for sufficiently large hypernets. We compare this method to standard hyperparameter optimization strategies.

Community Implementations: [ 1 code implementation](https://www.catalyzex.com/paper/arxiv:1802.09419/code)