{"id":"https://openalex.org/W7163913029","doi":"https://doi.org/10.48550/arxiv.2606.07134","title":"$\u03b1$-PFN: Fast Entropy Search via In-Context Learning","display_name":"$\u03b1$-PFN: Fast Entropy Search via In-Context Learning","publication_year":2026,"publication_date":"2026-06-05","ids":{"openalex":"https://openalex.org/W7163913029","doi":"https://doi.org/10.48550/arxiv.2606.07134"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.07134","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07134","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.07134","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5071156293","display_name":"Herilalaina Rakotoarison","orcid":"https://orcid.org/0000-0001-5267-7521"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Rakotoarison, Herilalaina","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5062314479","display_name":"Steven Adri\u00e6nsen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adriaensen, Steven","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5094097698","display_name":"Tom Viering","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Viering, Tom","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138197592","display_name":"Carl Hvarfner","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hvarfner, Carl","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047479105","display_name":"Samuel M\u00fcller","orcid":"https://orcid.org/0000-0002-3087-8127"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"M\u00fcller, Samuel","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5031002895","display_name":"Frank Hutter","orcid":"https://orcid.org/0000-0002-2037-3694"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hutter, Frank","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5006700143","display_name":"Eytan Bakshy","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Bakshy, Eytan","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":false,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.24410000443458557,"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/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.24410000443458557,"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/T12101","display_name":"Advanced Bandit Algorithms Research","score":0.1979999989271164,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.11659999936819077,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/entropy","display_name":"Entropy (arrow of time)","score":0.5777000188827515},{"id":"https://openalex.org/keywords/conditional-entropy","display_name":"Conditional entropy","score":0.4068000018596649},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.38760000467300415},{"id":"https://openalex.org/keywords/bayesian-optimization","display_name":"Bayesian optimization","score":0.38429999351501465},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.36649999022483826},{"id":"https://openalex.org/keywords/monte-carlo-method","display_name":"Monte Carlo method","score":0.34850001335144043},{"id":"https://openalex.org/keywords/data-acquisition","display_name":"Data acquisition","score":0.3312999904155731},{"id":"https://openalex.org/keywords/bayesian-network","display_name":"Bayesian network","score":0.3181000053882599},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.31130000948905945}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6715999841690063},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5777000188827515},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4884999990463257},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.42559999227523804},{"id":"https://openalex.org/C101721835","wikidata":"https://www.wikidata.org/wiki/Q813908","display_name":"Conditional entropy","level":3,"score":0.4068000018596649},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.38760000467300415},{"id":"https://openalex.org/C2778049539","wikidata":"https://www.wikidata.org/wiki/Q17002908","display_name":"Bayesian optimization","level":2,"score":0.38429999351501465},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.36649999022483826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3564999997615814},{"id":"https://openalex.org/C19499675","wikidata":"https://www.wikidata.org/wiki/Q232207","display_name":"Monte Carlo method","level":2,"score":0.34850001335144043},{"id":"https://openalex.org/C163985040","wikidata":"https://www.wikidata.org/wiki/Q1172399","display_name":"Data acquisition","level":2,"score":0.3312999904155731},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.3181000053882599},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C19889080","wikidata":"https://www.wikidata.org/wiki/Q2835852","display_name":"Beam search","level":3,"score":0.30959999561309814},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.3075000047683716},{"id":"https://openalex.org/C125583679","wikidata":"https://www.wikidata.org/wiki/Q755673","display_name":"Search algorithm","level":2,"score":0.30090001225471497},{"id":"https://openalex.org/C44415725","wikidata":"https://www.wikidata.org/wiki/Q4913893","display_name":"Binary entropy function","level":3,"score":0.29319998621940613},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C52622258","wikidata":"https://www.wikidata.org/wiki/Q131222","display_name":"Information theory","level":2,"score":0.2822999954223633},{"id":"https://openalex.org/C3020402766","wikidata":"https://www.wikidata.org/wiki/Q104376712","display_name":"Prior information","level":2,"score":0.2768000066280365},{"id":"https://openalex.org/C167981619","wikidata":"https://www.wikidata.org/wiki/Q1685498","display_name":"Cross entropy","level":3,"score":0.2759000062942505},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2721000015735626},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.2721000015735626},{"id":"https://openalex.org/C26713055","wikidata":"https://www.wikidata.org/wiki/Q245962","display_name":"Implementation","level":2,"score":0.259799987077713},{"id":"https://openalex.org/C106752470","wikidata":"https://www.wikidata.org/wiki/Q1364826","display_name":"Joint entropy","level":3,"score":0.2565999925136566},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.2558000087738037}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.07134","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07134","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.07134","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.07134","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Information-theoretic":[0],"acquisition":[1,59,129],"functions":[2,60],"such":[3],"as":[4],"Entropy":[5],"Search":[6],"(ES)":[7],"offer":[8],"a":[9,28,49,67,108,119],"principled":[10],"exploration-exploitation":[11],"framework":[12],"for":[13],"Bayesian":[14],"optimization":[15],"(BO).":[16],"However,":[17],"their":[18],"practical":[19],"implementation":[20],"relies":[21],"on":[22,79,97,141],"complicated":[23],"and":[24,42,127,143],"slow":[25],"approximations,":[26],"i.e.,":[27],"Monte":[29],"Carlo":[30],"estimation":[31],"of":[32],"the":[33,82,85,91,102,114,148],"information":[34,80,93,98],"gain.":[35],"This":[36],"complexity":[37],"can":[38],"introduce":[39],"numerical":[40],"errors":[41],"requires":[43],"specialized,":[44],"hand-crafted":[45],"implementations.":[46],"We":[47],"propose":[48],"two-stage":[50],"amortization":[51],"strategy":[52],"that":[53],"learns":[54],"to":[55,76,89],"approximate":[56],"entropy":[57,138,150],"search-based":[58],"using":[61],"Prior-data":[62],"Fitted":[63],"Networks":[64],"(PFNs)":[65],"in":[66],"single":[68,120],"forward":[69,121],"pass.":[70],"A":[71],"first":[72,103],"PFN":[73],"is":[74,87,134],"trained":[75,88],"be":[77],"conditioned":[78],"about":[81],"optima;":[83],"second,":[84],"$\u03b1$-PFN":[86,106],"predict":[90],"expected":[92],"gain":[94],"by":[95],"training":[96],"gains":[99],"measured":[100],"with":[101,118,136,157],"PFN.":[104],"The":[105],"offers":[107],"flexible":[109],"learned":[110],"approximation,":[111],"which":[112],"replaces":[113],"complex":[115],"heuristic":[116],"approximations":[117],"pass":[122],"per":[123],"candidate,":[124],"enabling":[125],"rapid":[126],"extensible":[128],"evaluation.":[130],"Empirically,":[131],"our":[132,155],"approach":[133],"competitive":[135],"state-of-the-art":[137],"search":[139,151],"implementations":[140],"synthetic":[142],"real-world":[144],"benchmarks,":[145],"while":[146],"accelerating":[147],"different":[149],"variants":[152],"across":[153],"all":[154],"experiments,":[156],"speed":[158],"ups":[159],"over":[160],"50x.":[161],"Source":[162],"code:":[163],"https://github.com/automl/AlphaPFN.":[164]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-09T00:00:00"}
