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Research map / Probabilistic and scientific modeling / Bayesian optimization

Modulating Surrogates for Bayesian Optimization

ICML 2020 · Probabilistic and scientific modeling › Bayesian optimization · approach: Acquisition function design · 1 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use latent-input modulation instead of a homoscedastic GPBayesian optimization with GP surrogates · Corrupted Holder Table, 50 evalsmean gap: 0.826 -> 0.896Helped
Use latent-input modulation instead of a GPBayesian optimization with GP surrogates · Shubert, 50 evalsmean gap: 0.378 -> 0.593Helped
Use latent-input modulation instead of a GPBayesian optimization with GP surrogates · Holder Table, 50 evalsmean gap: 0.939 -> 0.993Helped
Use latent-input modulation instead of a homoscedastic GPBayesian optimization with GP surrogates · Ackley [-10, 30]^d, 100 evals, 6Dmean gap: 0.789 -> 0.712Hurt
Use latent-input modulation instead of a homoscedastic GPBayesian optimization with GP surrogates · Weierstrass, 100 evalsmean gap: 0.704 -> 0.625Hurt
Use latent-input modulation instead of a homoscedastic GPBayesian optimization with GP surrogates · Deflected Corrugated Spring, 100 evalsmean gap: 0.840 -> 0.697Hurt
Use latent-input modulation instead of a GPBayesian optimization with GP surrogates · Shubert [-10, 10]^d, 100 evalsmean gap: 0.510 -> 0.877Helped
Use latent-input modulation instead of a homoscedastic GPBayesian optimization with GP surrogates · Corrupted Holder Table, 100 evalsmean gap: 0.889 -> 0.918Helped
Use latent-input modulation instead of a homoscedastic GPBayesian optimization with GP surrogates · Corrupted Exponential, 100 evalsmean gap: 0.847 -> 0.806Hurt
Use latent-input modulation instead of a heteroscedastic GPBayesian optimization with GP surrogates · Active learning: Robot Pushing, 100 evalsmean gap: 0.907 -> 0.932Helped
Use latent-input modulation rather than the standard GPBayesian optimization with GP surrogates · Across the benchmark suitemean gapMixed

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