{"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/pg-lbo-enhancing-high-dimensional-bayesian","title":"PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-Label and Gaussian Process Guidance","arxiv_id":"2312.16983","date":"2023-12-28","proceeding":null,"authors":["Taicai Chen","Yue Duan","Dong Li","Lei Qi","Yinghuan Shi","Yang Gao"],"abstract":"Variational Autoencoder based Bayesian Optimization (VAE-BO) has demonstrated its excellent performance in addressing high-dimensional structured optimization problems. However, current mainstream methods overlook the potential of utilizing a pool of unlabeled data to construct the latent space, while only concentrating on designing sophisticated models to leverage the labeled data. Despite their effective usage of labeled data, these methods often require extra network structures, additional procedure, resulting in computational inefficiency. To address this issue, we propose a novel method to effectively utilize unlabeled data with the guidance of labeled data. Specifically, we tailor the pseudo-labeling technique from semi-supervised learning to explicitly reveal the relative magnitudes of optimization objective values hidden within the unlabeled data. Based on this technique, we assign appropriate training weights to unlabeled data to enhance the construction of a discriminative latent space. Furthermore, we treat the VAE encoder and the Gaussian Process (GP) in Bayesian optimization as a unified deep kernel learning process, allowing the direct utilization of labeled data, which we term as Gaussian Process guidance. This directly and effectively integrates the goal of improving GP accuracy into the VAE training, thereby guiding the construction of the latent space. The extensive experiments demonstrate that our proposed method outperforms existing VAE-BO algorithms in various optimization scenarios. Our code will be published at https://github.com/TaicaiChen/PG-LBO.","url_abs":"https://arxiv.org/abs/2312.16983v1","url_pdf":"https://arxiv.org/pdf/2312.16983v1.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":"pg-lbo-enhancing-high-dimensional-bayesian","repo_url":"https://github.com/taicaichen/pg-lbo","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"pytorch","reach":{"status":"ok","spdx":"MIT"}}],"tasks":[{"task_slug":"bayesian-optimization","task_name":"Bayesian Optimization"},{"task_slug":"pseudo-label","task_name":"Pseudo Label"}],"methods":[{"method_slug":"gaussian-process","method_name":"Gaussian Process"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=2312.16983","mcp":{"get_harvested_code_for_paper":{"arxiv_id":"2312.16983"}},"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/taicaichen/pg-lbo","reach":{"status":"ok","spdx":"MIT"}}],"summary":{"ran":9,"unverified":1},"by_repo_kind":{"official":{"samples":10,"ran":9,"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":0,"samples":[{"code_sha256_prefix":"53a5d876d2a4c4f0","entry":"cummax","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"utils/utils_plot.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/utils/utils_plot.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"53a5d876d2a4c4f0"}},{"code_sha256_prefix":"7f3c23e38b92bd2d","entry":"get_cummax","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"utils/utils_plot.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/utils/utils_plot.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7f3c23e38b92bd2d"}},{"code_sha256_prefix":"428d2d665b6f8606","entry":"get_cummin","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"utils/utils_plot.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/utils/utils_plot.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"428d2d665b6f8606"}},{"code_sha256_prefix":"d6905397be142ee2","entry":"get_props","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"weighted_retraining/weighted_retraining/utils.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/weighted_retraining/weighted_retraining/utils.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"d6905397be142ee2"}},{"code_sha256_prefix":"36222d331dfa0db0","entry":"load_w_pickle","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"utils/utils_save.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/utils/utils_save.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"36222d331dfa0db0"}},{"code_sha256_prefix":"721ea6828e17a8cb","entry":"parse_dict","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"utils/utils_cmd.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/utils/utils_cmd.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"721ea6828e17a8cb"}},{"code_sha256_prefix":"c4ee8b7e136f578e","entry":"parse_list","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"utils/utils_cmd.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/utils/utils_cmd.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"c4ee8b7e136f578e"}},{"code_sha256_prefix":"910a8a6266051c6c","entry":"query_covar","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"weighted_retraining/weighted_retraining/bo_torch/gp_torch.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/weighted_retraining/weighted_retraining/bo_torch/gp_torch.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"910a8a6266051c6c"}},{"code_sha256_prefix":"8180e80fca9e2f95","entry":"str_dict","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"utils/utils_save.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/utils/utils_save.py","link_basis":"first_harvest_node","language":"python","status":"ran","verification_level":1,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"8180e80fca9e2f95"}},{"code_sha256_prefix":"7e54179509606534","entry":"run_command","repo":"taicaichen/pg-lbo","repo_kind":"official","path":"utils/utils_cmd.py","file_url":"https://github.com/taicaichen/pg-lbo/blob/HEAD/utils/utils_cmd.py","link_basis":"first_harvest_node","language":"python","status":"unverified","verification_level":0,"contract_check":null,"metamorphic_tier":null,"behaviour_fingerprint":false,"licence":"MIT","inline_ok":true,"mcp_get_code":{"code_sha256":"7e54179509606534"}}]},"arxiv_metadata":null,"syntology_extracted_results":null}