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Black-Box Optimization with Local Generative Surrogates

Black-Box Optimization with Local Generative Surrogates

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2354571941

Black-Box Optimization with Local Generative Surrogates

About this item

Full title

Black-Box Optimization with Local Generative Surrogates

Publisher

Ithaca: Cornell University Library, arXiv.org

Journal title

arXiv.org, 2020-06

Language

English

Formats

Publication information

Publisher

Ithaca: Cornell University Library, arXiv.org

More information

Scope and Contents

Contents

We propose a novel method for gradient-based optimization of black-box simulators using differentiable local surrogate models. In fields such as physics and engineering, many processes are modeled with non-differentiable simulators with intractable likelihoods. Optimization of these forward models is particularly challenging, especially when the si...

Alternative Titles

Full title

Black-Box Optimization with Local Generative Surrogates

Identifiers

Primary Identifiers

Record Identifier

TN_cdi_proquest_journals_2354571941

Permalink

https://devfeature-collection.sl.nsw.gov.au/record/TN_cdi_proquest_journals_2354571941

Other Identifiers

E-ISSN

2331-8422

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