{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:02:05Z","timestamp":1750309325082,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":19,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,7,14]],"date-time":"2024-07-14T00:00:00Z","timestamp":1720915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["2102406"],"award-info":[{"award-number":["2102406"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,7,14]]},"DOI":"10.1145\/3638530.3654357","type":"proceedings-article","created":{"date-parts":[[2024,8,1]],"date-time":"2024-08-01T14:54:43Z","timestamp":1722524083000},"page":"451-454","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Implicit Symbolic Regression via Probabilistic Fitness"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5245-5769","authenticated-orcid":false,"given":"Graham","family":"Roberts","sequence":"first","affiliation":[{"name":"University of Connecticut, Storrs, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-8792-6853","authenticated-orcid":false,"given":"Everett","family":"Grethel","sequence":"additional","affiliation":[{"name":"University of Connecticut, Storrs, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5519-1092","authenticated-orcid":false,"given":"Qian","family":"Yang","sequence":"additional","affiliation":[{"name":"University of Connecticut, Storrs, United States of America"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,8]]},"reference":[{"volume-title":"Pattern Recognition and Machine Learning","author":"Bishop Christopher M.","key":"e_1_3_2_2_1_1","unstructured":"Christopher M. Bishop. 2006. Pattern Recognition and Machine Learning. Springer, New York."},{"key":"e_1_3_2_2_2_1","doi-asserted-by":"publisher","DOI":"10.1073\/pnas.1517384113"},{"key":"e_1_3_2_2_3_1","volume-title":"Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. ArXiv abs\/2305.01582","author":"Cranmer M.","year":"2023","unstructured":"M. Cranmer. 2023. Interpretable Machine Learning for Science with PySR and SymbolicRegression.jl. ArXiv abs\/2305.01582 (2023)."},{"volume-title":"Proceedings of the Genetic and Evolutionary Computation Conference Companion.","author":"Huynh Singh H.","key":"e_1_3_2_2_4_1","unstructured":"Singh H. Huynh, Q. N. and T. Ray. 2022. Discovery of implicit relationships from data using linear programming and mixed integer linear programming. In Proceedings of the Genetic and Evolutionary Computation Conference Companion."},{"key":"e_1_3_2_2_5_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-023-28328-2"},{"key":"e_1_3_2_2_6_1","doi-asserted-by":"publisher","DOI":"10.5555\/2503308.2503323"},{"key":"e_1_3_2_2_7_1","volume-title":"Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks.","author":"Cava William La","year":"2021","unstructured":"William La Cava, Patryk Orzechowski, Bogdan Burlacu, Fabricio de Franca, Marco Virgolin, Ying Jin, Michael Kommenda, and Jason Moore. 2021. Contemporary Symbolic Regression Methods and their Relative Performance. In Proceedings of the Neural Information Processing Systems Track on Datasets and Benchmarks."},{"key":"e_1_3_2_2_8_1","volume-title":"Petersen","author":"Landajuela Mikel","year":"2022","unstructured":"Mikel Landajuela, Chak Lee, Jiachen Yang, Ruben Glatt, Claudio P. Santiago, Ignacio Aravena, Terrell N. Mundhenk, Garrett Mulcahy, and Brenden K. Petersen. 2022. A Unified Framework for Deep Symbolic Regression. In Advances in Neural Information Processing Systems."},{"key":"e_1_3_2_2_9_1","volume-title":"Saketh Bharadwaj, Sam Silva, and Max Tegmark.","author":"Liu Ziming","year":"2023","unstructured":"Ziming Liu, Patrick Obin Sturm, Saketh Bharadwaj, Sam Silva, and Max Tegmark. 2023. Discovering New Interpretable Conservation Laws as Sparse Invariants. (2023)."},{"key":"e_1_3_2_2_10_1","doi-asserted-by":"crossref","unstructured":"Zhibin Miao Jinghui Zhong Peng Yang Shibin Wang and Dong Liu. 2021. Implicit Neural Network for Implicit Data Regression Problems. In ICONIP.","DOI":"10.1007\/978-3-030-92307-5_22"},{"key":"e_1_3_2_2_11_1","unstructured":"Art B. Owen. 2013. Monte Carlo theory methods and examples. Chapter 9. \"https:\/\/artowen.su.domains\/mc\/\"."},{"key":"e_1_3_2_2_12_1","volume-title":"DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 165--174","author":"Park Jeong Joon","year":"2019","unstructured":"Jeong Joon Park, Peter Florence, Julian Straub, Richard Newcombe, and Steven Lovegrove. 2019. DeepSDF: Learning Continuous Signed Distance Functions for Shape Representation. In 2019 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR). 165--174."},{"key":"e_1_3_2_2_13_1","volume-title":"International Conference on Learning Representations.","author":"Petersen Brenden K","year":"2021","unstructured":"Brenden K Petersen, Mikel Landajuela Larma, Terrell N. Mundhenk, Claudio Prata Santiago, Soo Kyung Kim, and Joanne Taery Kim. 2021. Deep symbolic regression: Recovering mathematical expressions from data via risk-seeking policy gradients. In International Conference on Learning Representations."},{"key":"e_1_3_2_2_14_1","doi-asserted-by":"publisher","DOI":"10.1126\/science.1165893"},{"volume-title":"Symbolic Regression of Implicit Equations","author":"Schmidt Michael","key":"e_1_3_2_2_15_1","unstructured":"Michael Schmidt and Hod Lipson. 2010. Symbolic Regression of Implicit Equations. Springer US, Boston, MA, 73--85."},{"key":"e_1_3_2_2_16_1","volume-title":"Advances in Neural Information Processing Systems (NeurIPS","author":"Udrescu Silviu-Marian","year":"2020","unstructured":"Silviu-Marian Udrescu, Andrew Tan, Jianhai Feng, Orisvaldo Neto, Tailin Wu, and Max Tegmark. 2020. AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity. In Advances in Neural Information Processing Systems (NeurIPS 2020)."},{"volume-title":"Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '95)","author":"Veach Eric","key":"e_1_3_2_2_17_1","unstructured":"Eric Veach and Leonidas J. Guibas. 1995. Optimally Combining Sampling Techniques for Monte Carlo Rendering. In Proceedings of the 22nd Annual Conference on Computer Graphics and Interactive Techniques (SIGGRAPH '95). Association for Computing Machinery, 419--428."},{"key":"e_1_3_2_2_18_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41467-022-35084-w"},{"key":"e_1_3_2_2_19_1","doi-asserted-by":"publisher","DOI":"10.1109\/TETC.2021.3068651"}],"event":{"name":"GECCO '24 Companion: Genetic and Evolutionary Computation Conference Companion","sponsor":["SIGEVO ACM Special Interest Group on Genetic and Evolutionary Computation"],"location":"Melbourne VIC Australia","acronym":"GECCO '24 Companion"},"container-title":["Proceedings of the Genetic and Evolutionary Computation Conference Companion"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638530.3654357","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3638530.3654357","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3638530.3654357","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:04:01Z","timestamp":1750291441000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3638530.3654357"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,7,14]]},"references-count":19,"alternative-id":["10.1145\/3638530.3654357","10.1145\/3638530"],"URL":"https:\/\/doi.org\/10.1145\/3638530.3654357","relation":{},"subject":[],"published":{"date-parts":[[2024,7,14]]},"assertion":[{"value":"2024-08-01","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}