{"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/high-dimensional-bayesian-optimization-with-2","title":"High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces","arxiv_id":"2103.00349","date":"2021-02-27","proceeding":null,"authors":["David Eriksson","Martin Jankowiak"],"abstract":"Bayesian optimization (BO) is a powerful paradigm for efficient optimization of black-box objective functions. High-dimensional BO presents a particular challenge, in part because the curse of dimensionality makes it difficult to define -- as well as do inference over -- a suitable class of surrogate models. We argue that Gaussian process surrogate models defined on sparse axis-aligned subspaces offer an attractive compromise between flexibility and parsimony. We demonstrate that our approach, which relies on Hamiltonian Monte Carlo for inference, can rapidly identify sparse subspaces relevant to modeling the unknown objective function, enabling sample-efficient high-dimensional BO. In an extensive suite of experiments comparing to existing methods for high-dimensional BO we demonstrate that our algorithm, Sparse Axis-Aligned Subspace BO (SAASBO), achieves excellent performance on several synthetic and real-world problems without the need to set problem-specific hyperparameters.","url_abs":"https://arxiv.org/abs/2103.00349v2","url_pdf":"https://arxiv.org/pdf/2103.00349v2.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":"high-dimensional-bayesian-optimization-with-2","repo_url":"https://github.com/martinjankowiak/saasbo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"jax","reach":null},{"paper_slug":"high-dimensional-bayesian-optimization-with-2","repo_url":"https://github.com/xzt008/standard-gp-is-all-you-need-for-hdbo","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"high","task_name":"Vocal Bursts Intensity Prediction"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2103.00349","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2103.00349"}},"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. Samples come from repositories linked to the paper, official or community; repo_kind says which.","repos":[{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/martinjankowiak/saasbo","reach":null},{"provenance":"external:paperswithcode_snapshot_2025-07-28","url":"https://github.com/xzt008/standard-gp-is-all-you-need-for-hdbo","reach":null}],"summary":{"ran_draft_wrong":2,"unverified":1},"by_repo_kind":{"official":{"samples":3,"ran":2,"repositories":1}},"repo_kind_vocabulary":{"official":"The archive marks this repository official for the paper","named_in_paper":"The archive records that the paper mentions this repository; it is not marked official","listed":"In the archive's code links for this paper, not marked official and not recorded as mentioned in the paper","found_in_text":"Syntology found this repository in the paper's own text; whether it is the authors' implementation is not asserted","community":"Not in the archive's code links for this paper; a community repository Syntology harvested"},"n_pointer_only_for_licence":3,"samples":[{"code_sha256_prefix":"8ba5cf3cb6e6ca73","entry":"ei","repo":"martinjankowiak/saasbo","repo_kind":"official","path":"saasbo.py","file_url":"https://github.com/martinjankowiak/saasbo/blob/HEAD/saasbo.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"8ba5cf3cb6e6ca73"}},{"code_sha256_prefix":"ccf046d24a34fc25","entry":"ei_grad","repo":"martinjankowiak/saasbo","repo_kind":"official","path":"saasbo.py","file_url":"https://github.com/martinjankowiak/saasbo/blob/HEAD/saasbo.py","link_basis":"first_harvest_node","language":"python","status":"ran_draft_wrong","verification_level":1,"contract_check":"MISDECLARED","metamorphic_tier":"invariant","behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"ccf046d24a34fc25"}},{"code_sha256_prefix":"6f31b84a9aeae24c","entry":"optimize_ei","repo":"martinjankowiak/saasbo","repo_kind":"official","path":"saasbo.py","file_url":"https://github.com/martinjankowiak/saasbo/blob/HEAD/saasbo.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"NONE","inline_ok":false,"mcp_get_code":{"code_sha256":"6f31b84a9aeae24c"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}