{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T10:09:20Z","timestamp":1761818960987,"version":"build-2065373602"},"reference-count":45,"publisher":"IOP Publishing","issue":"4","license":[{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,10,30]],"date-time":"2025-10-30T00:00:00Z","timestamp":1761782400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/iopscience.iop.org\/info\/page\/text-and-data-mining"}],"content-domain":{"domain":["iopscience.iop.org"],"crossmark-restriction":false},"short-container-title":["Mach. Learn.: Sci. Technol."],"published-print":{"date-parts":[[2025,12,30]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>\n                    Bayesian Optimisation (BO) is a sample-efficient method for optimising expensive black-box functions, making it particularly suitable for engineering problems where gradients are unavailable and evaluating the objective or constraints is computationally costly. However, such problems often involve high-dimensional inputs and a large number of constraints, posing significant challenges for standard BO frameworks. While prior research has addressed scalability with respect to high-dimensional inputs in constrained settings, efficiently handling large numbers of constraints, i.e. high-dimensional outputs, remains an open problem. This work introduces\n                    <jats:bold>A<\/jats:bold>\n                    utoencoder-\n                    <jats:bold>E<\/jats:bold>\n                    nhanced Joint Dimensionality\n                    <jats:bold>R<\/jats:bold>\n                    eduction for C\n                    <jats:bold>o<\/jats:bold>\n                    nstrained BO (AERO-BO), a framework that performs dimensionality reduction in both the input (design variable) and output (objective and constraint) spaces via autoencoders. These autoencoders are trained online, requiring no pre-training, and their respective latent representations are connected through Gaussian Processes, which serve as surrogate models during optimisation. By operating in a joint latent space, AERO-BO enables scalable and efficient optimisation in settings with hundreds of design variables and thousands of black-box constraints.\n                  <\/jats:p>","DOI":"10.1088\/2632-2153\/ae0efe","type":"journal-article","created":{"date-parts":[[2025,10,2]],"date-time":"2025-10-02T22:57:28Z","timestamp":1759445848000},"page":"045028","update-policy":"https:\/\/doi.org\/10.1088\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Autoencoder-enhanced joint dimensionality reduction for constrained Bayesian optimisation"],"prefix":"10.1088","volume":"6","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7002-4823","authenticated-orcid":true,"given":"Hauke","family":"Maathuis","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7882-2173","authenticated-orcid":false,"given":"Roeland De","family":"Breuker","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9711-0991","authenticated-orcid":true,"given":"Saullo G P","family":"Castro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"266","published-online":{"date-parts":[[2025,10,30]]},"reference":[{"article-title":"Unexpected improvements to expected improvement for Bayesian optimization","year":"2023","author":"Ament","key":"mlstae0efebib1","type":"conference-proceedings"},{"article-title":"The MOPTA 2008 Benchmark","year":"2008","author":"Anjos","key":"mlstae0efebib2","type":"other"},{"key":"mlstae0efebib3","type":"conference-proceedings","article-title":"BoTorch: a framework for efficient Monte-Carlo Bayesian optimization","volume":"vol 33","author":"Balandat","year":"2020"},{"key":"mlstae0efebib4","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3545611","type":"journal-article","article-title":"A survey on high-dimensional Gaussian process modeling with application to Bayesian optimization","volume":"2","author":"Binois","year":"2022","journal-title":"ACM Trans. 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