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An Empirical Bayes Approach to Optimizing Machine Learning Algorithms

NeurIPS 2017 · Probabilistic and scientific modeling › Bayesian optimization · approach: Multi-fidelity hyperparameter optimization

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
replace BayesOpt hyperparameter selection with EB-Hyp averaging, no validation setLDA · 20 Newsgroupspredictive log likelihood: -361661 -> -351911Helped
use EB-Hyp instead of BayesOpt when a validation set is availableLDA · 20 Newsgroupspredictive log likelihood: -357648 -> -357650No clear effect
remove the separate validation set from BayesOpt (ablation)BayesOpt · LDA 20 Newsgroupspredictive log likelihood: -357648 -> -361661Hurt
remove the separate validation set from EB-Hyp (ablation)EB-Hyp · LDA 20 Newsgroupspredictive log likelihood: -357650 -> -351911Helped
replace BayesOpt with EB-Hyp, both without validationDLGM · Labeled Faces in the Wildpredictive log likelihood: -15970 -> -15872Helped
replace BayesOpt with EB-Hyp, both with validationDLGM · Labeled Faces in the Wildpredictive log likelihood: -17071 -> -16375Helped
remove the validation set from BayesOpt (ablation)BayesOpt · DLGM Labeled Faces in the Wildpredictive log likelihood: -17071 -> -15970Helped
remove the validation set from EB-Hyp (ablation)EB-Hyp · DLGM Labeled Faces in the Wildpredictive log likelihood: -16375 -> -15872Helped
random hyperparameter search instead of BayesOptLDA · 20 Newsgroupspredictive log likelihood: -357648 -> -2666074Hurt
random hyperparameter search instead of BayesOptDLGM · Labeled Faces in the Wildpredictive log likelihood: -17071 -> -17271Hurt

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