{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/distributionally-ambiguous-optimization","title":"Distributionally Ambiguous Optimization Techniques for Batch Bayesian Optimization","arxiv_id":"1707.04191","date":"2017-07-13","proceeding":null,"authors":["Nikitas Rontsis","Michael A. Osborne","Paul J. Goulart"],"abstract":"We propose a novel, theoretically-grounded, acquisition function for Batch\nBayesian optimization informed by insights from distributionally ambiguous\noptimization. Our acquisition function is a lower bound on the well-known\nExpected Improvement function, which requires evaluation of a Gaussian\nExpectation over a multivariate piecewise affine function. Our bound is\ncomputed instead by evaluating the best-case expectation over all probability\ndistributions consistent with the same mean and variance as the original\nGaussian distribution. Unlike alternative approaches, including Expected\nImprovement, our proposed acquisition function avoids multi-dimensional\nintegrations entirely, and can be computed exactly - even on large batch sizes\n- as the solution of a tractable convex optimization problem. Our suggested\nacquisition function can also be optimized efficiently, since first and second\nderivative information can be calculated inexpensively as by-products of the\nacquisition function calculation itself. We derive various novel theorems that\nground our work theoretically and we demonstrate superior performance via\nsimple motivating examples, benchmark functions and real-world problems.","url_abs":"http://arxiv.org/abs/1707.04191v4","url_pdf":"http://arxiv.org/pdf/1707.04191v4.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"distributionally-ambiguous-optimization","repo_url":"https://github.com/oxfordcontrol/Bayesian-Optimization","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1707.04191","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"1707.04191"}},"developers":"https://syntology.ai/developers","read_at":"2026-09-24T18:15:14+00:00","read_at_is":"when the build read Syntology's graph, not when any sample ran","claim":"Per-sample execution status on synthesized fixtures; not a correctness claim about the paper. 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