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Dana Brooks

2 accepted papers

2021

Faster & More Reliable Tuning of Neural Networks: Bayesian Optimization with Importance Sampling

AISTATS 2021poster

Many contemporary machine learning models require extensive tuning of hyperparameters to perform well. A variety of methods, such as Bayesian optimization, have been developed to automate and expedite this process. However, tuning remains extremely costly as it typically requires repeatedly fully tr…

2021

Rate-Regularization and Generalization in Variational Autoencoders

AISTATS 2021poster

Variational autoencoders (VAEs) optimize an objective that comprises a reconstruction loss (the distortion) and a KL term (the rate). The rate is an upper bound on the mutual information, which is often interpreted as a regularizer that controls the degree of compression. We here examine whether inc…