{"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/active-expansion-sampling-for-learning","title":"Active Expansion Sampling for Learning Feasible Domains in an Unbounded Input Space","arxiv_id":"1708.07888","date":"2017-08-25","proceeding":null,"authors":["Wei Chen","Mark Fuge"],"abstract":"Many engineering problems require identifying feasible domains under implicit\nconstraints. One example is finding acceptable car body styling designs based\non constraints like aesthetics and functionality. Current active-learning based\nmethods learn feasible domains for bounded input spaces. However, we usually\nlack prior knowledge about how to set those input variable bounds. Bounds that\nare too small will fail to cover all feasible domains; while bounds that are\ntoo large will waste query budget. To avoid this problem, we introduce Active\nExpansion Sampling (AES), a method that identifies (possibly disconnected)\nfeasible domains over an unbounded input space. AES progressively expands our\nknowledge of the input space, and uses successive exploitation and exploration\nstages to switch between learning the decision boundary and searching for new\nfeasible domains. We show that AES has a misclassification loss guarantee\nwithin the explored region, independent of the number of iterations or labeled\nsamples. Thus it can be used for real-time prediction of samples' feasibility\nwithin the explored region. We evaluate AES on three test examples and compare\nAES with two adaptive sampling methods -- the Neighborhood-Voronoi algorithm\nand the straddle heuristic -- that operate over fixed input variable bounds.","url_abs":"http://arxiv.org/abs/1708.07888v3","url_pdf":"http://arxiv.org/pdf/1708.07888v3.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":"active-expansion-sampling-for-learning","repo_url":"https://github.com/IDEALLab/Active-Expansion-Sampling","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"active-learning","task_name":"Active Learning"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}