{"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/atpboost-learning-premise-selection-in-binary","title":"ATPboost: Learning Premise Selection in Binary Setting with ATP Feedback","arxiv_id":"1802.03375","date":"2018-02-09","proceeding":null,"authors":["Bartosz Piotrowski","Josef Urban"],"abstract":"ATPboost is a system for solving sets of large-theory problems by\ninterleaving ATP runs with state-of-the-art machine learning of premise\nselection from the proofs. Unlike many previous approaches that use multi-label\nsetting, the learning is implemented as binary classification that estimates\nthe pairwise-relevance of (theorem, premise) pairs. ATPboost uses for this the\nXGBoost gradient boosting algorithm, which is fast and has state-of-the-art\nperformance on many tasks. Learning in the binary setting however requires\nnegative examples, which is nontrivial due to many alternative proofs. We\ndiscuss and implement several solutions in the context of the ATP/ML feedback\nloop, and show that ATPboost with such methods significantly outperforms the\nk-nearest neighbors multilabel classifier.","url_abs":"http://arxiv.org/abs/1802.03375v1","url_pdf":"http://arxiv.org/pdf/1802.03375v1.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":"atpboost-learning-premise-selection-in-binary","repo_url":"https://github.com/BartoszPiotrowski/ATPboost","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":1,"framework":"none","reach":{"status":"unanswered"}}],"tasks":[{"task_slug":"binary-classification","task_name":"Binary Classification"},{"task_slug":"classification","task_name":"General Classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":"https://app.syntology.ai/?focus=1802.03375","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}