{"id":"https://openalex.org/W4389325880","doi":"https://doi.org/10.48550/arxiv.2312.00396","title":"GFN-SR: Symbolic Regression with Generative Flow Networks","display_name":"GFN-SR: Symbolic Regression with Generative Flow Networks","publication_year":2023,"publication_date":"2023-12-01","ids":{"openalex":"https://openalex.org/W4389325880","doi":"https://doi.org/10.48550/arxiv.2312.00396"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2312.00396","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.00396","pdf_url":"https://arxiv.org/pdf/2312.00396","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2312.00396","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5045113361","display_name":"Sida Li","orcid":"https://orcid.org/0000-0003-0888-5838"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Sida","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021218137","display_name":"Ioana Marinescu","orcid":"https://orcid.org/0000-0001-9131-6390"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Marinescu, Ioana","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5025550714","display_name":"Sebastian Musslick","orcid":"https://orcid.org/0000-0002-8896-639X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Musslick, Sebastian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12535","display_name":"Machine Learning and Data Classification","score":0.9850000143051147,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11975","display_name":"Evolutionary Algorithms and Applications","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10462","display_name":"Reinforcement Learning in Robotics","score":0.9614999890327454,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/symbolic-regression","display_name":"Symbolic regression","score":0.6890017986297607},{"id":"https://openalex.org/keywords/directed-acyclic-graph","display_name":"Directed acyclic graph","score":0.6529756784439087},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5823318958282471},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5529636144638062},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.521906852722168},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.49653512239456177},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.4721517562866211},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.4513470530509949},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4388432204723358},{"id":"https://openalex.org/keywords/traverse","display_name":"Traverse","score":0.41720888018608093},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.4166063666343689},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.3548945188522339},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.29249879717826843},{"id":"https://openalex.org/keywords/genetic-programming","display_name":"Genetic programming","score":0.2674717307090759},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2489604949951172},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11268523335456848}],"concepts":[{"id":"https://openalex.org/C2776400721","wikidata":"https://www.wikidata.org/wiki/Q18171762","display_name":"Symbolic regression","level":3,"score":0.6890017986297607},{"id":"https://openalex.org/C74197172","wikidata":"https://www.wikidata.org/wiki/Q1195339","display_name":"Directed acyclic graph","level":2,"score":0.6529756784439087},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5823318958282471},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5529636144638062},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.521906852722168},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.49653512239456177},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.4721517562866211},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.4513470530509949},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4388432204723358},{"id":"https://openalex.org/C176809094","wikidata":"https://www.wikidata.org/wiki/Q15401496","display_name":"Traverse","level":2,"score":0.41720888018608093},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.4166063666343689},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3548945188522339},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29249879717826843},{"id":"https://openalex.org/C110332635","wikidata":"https://www.wikidata.org/wiki/Q629498","display_name":"Genetic programming","level":2,"score":0.2674717307090759},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2489604949951172},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11268523335456848},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2312.00396","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.00396","pdf_url":"https://arxiv.org/pdf/2312.00396","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2312.00396","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2312.00396","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2312.00396","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2312.00396","pdf_url":"https://arxiv.org/pdf/2312.00396","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320309070","display_name":"Brown University","ror":"https://ror.org/05gq02987"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4389325880.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2377402383","https://openalex.org/W2380835401","https://openalex.org/W2381912691","https://openalex.org/W2350381577","https://openalex.org/W2353618196","https://openalex.org/W2348074676","https://openalex.org/W2385033175","https://openalex.org/W2374043190","https://openalex.org/W2363298784","https://openalex.org/W2075533215"],"abstract_inverted_index":{"Symbolic":[0],"regression":[1,39],"(SR)":[2],"is":[3,114],"an":[4,67,82,108],"area":[5],"of":[6,18,28,81,116,121,145,150],"interpretable":[7],"machine":[8],"learning":[9,54],"that":[10,21,94,127],"aims":[11],"to":[12,55,71,101,138,141],"identify":[13],"mathematical":[14],"expressions,":[15],"often":[16],"composed":[17],"simple":[19],"functions,":[20],"best":[22],"fit":[23],"in":[24,47,133],"a":[25,44,88,98,118,143,148],"given":[26],"set":[27,120],"covariates":[29],"$X$":[30],"and":[31],"response":[32],"$y$.":[33],"In":[34,62],"recent":[35],"years,":[36],"deep":[37,52,75],"symbolic":[38],"(DSR)":[40],"has":[41],"emerged":[42],"as":[43,85],"popular":[45],"method":[46,113],"the":[48,57,79],"field":[49],"by":[50],"leveraging":[51],"reinforcement":[53],"solve":[56],"complicated":[58],"combinatorial":[59],"search":[60],"problem.":[61],"this":[63],"work,":[64],"we":[65,125],"propose":[66],"alternative":[68],"framework":[69],"(GFN-SR)":[70],"approach":[72],"SR":[73,131],"with":[74,107],"learning.":[76],"We":[77],"model":[78],"construction":[80],"expression":[83],"tree":[84],"traversing":[86],"through":[87],"directed":[89],"acyclic":[90],"graph":[91],"(DAG)":[92],"so":[93],"GFlowNet":[95],"can":[96],"learn":[97,142],"stochastic":[99],"policy":[100],"generate":[102],"such":[103],"trees":[104],"sequentially.":[105],"Enhanced":[106],"adaptive":[109],"reward":[110],"baseline,":[111],"our":[112],"capable":[115],"generating":[117],"diverse":[119],"best-fitting":[122],"expressions.":[123],"Notably,":[124],"observe":[126],"GFN-SR":[128],"outperforms":[129],"other":[130],"algorithms":[132],"noisy":[134],"data":[135],"regimes,":[136],"owing":[137],"its":[139],"ability":[140],"distribution":[144],"rewards":[146],"over":[147],"space":[149],"candidate":[151],"solutions.":[152]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2025-10-10T00:00:00"}