Article

We introduce a model-based method for asynchronous multi-fidelity hyperparameter and neural architecture search that combines the strengths of asynchronous Hyperband and Gaussian process-based Bayesian optimization, achieving substantial speed-ups over current state-of-the-art methods on challenging benchmarks for tabular data, image classification, and language modeling.

We formalize the problem of learning interdomain correspondences in the absence of paired data as Bayesian inference in a latent …