{"id":"https://openalex.org/W7224110993","doi":"https://doi.org/10.5281/zenodo.23720855","title":"Lookahead Prediction-Parameterization Intervention Optimization for Diffusion Models: Predicting Downstream Effects of Alternative Prediction Representations to Select Adaptive Sampling Computations","display_name":"Lookahead Prediction-Parameterization Intervention Optimization for Diffusion Models: Predicting Downstream Effects of Alternative Prediction Representations to Select Adaptive Sampling Computations","publication_year":2026,"publication_date":"2026-10-28","ids":{"openalex":"https://openalex.org/W7224110993","doi":"https://doi.org/10.5281/zenodo.23720855"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.23720855","doi":"https://doi.org/10.5281/zenodo.23720855","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23720855","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.23720855","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100638045","display_name":"Jincheng Zhang","orcid":"https://orcid.org/0000-0001-7332-6704","observed_orcids":["https://orcid.org/0000-0001-7332-6704","https://orcid.org/0009-0005-1860-0009"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhang, Jincheng","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0005-1860-0009","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5100638045"],"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9955999851226807,"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"}},"topics":[{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9955999851226807,"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"}},{"id":"https://openalex.org/T10688","display_name":"Image and Signal Denoising Methods","score":0.002199999988079071,"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"}},{"id":"https://openalex.org/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.0005000000237487257,"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/diffusion-models","display_name":"diffusion models","score":0.9869999885559082},{"id":"https://openalex.org/keywords/noise-prediction","display_name":"noise prediction","score":0.8140000104904175},{"id":"https://openalex.org/keywords/speed-prediction","display_name":"velocity prediction","score":0.9980000257492065},{"id":"https://openalex.org/keywords/adaptive-sampling","display_name":"adaptive sampling","score":0.6100000143051147},{"id":"https://openalex.org/keywords/inference-optimization","display_name":"inference optimization","score":0.5709999799728394},{"id":"https://openalex.org/keywords/numerical-stability","display_name":"numerical stability","score":0.8149999976158142},{"id":"https://openalex.org/keywords/error-propagation","display_name":"error propagation","score":0.8100000023841858},{"id":"https://openalex.org/keywords/noise-reduction","display_name":"noise reduction","score":0.6150000095367432},{"id":"https://openalex.org/keywords/computational-cost","display_name":"computational cost","score":0.6499999761581421}],"concepts":[{"id":"https://openalex.org/C2778770139","wikidata":"https://www.wikidata.org/wiki/Q1966904","display_name":"Solver","level":2,"score":0.5720000267028809},{"id":"https://openalex.org/C185429906","wikidata":"https://www.wikidata.org/wiki/Q1130160","display_name":"Estimator","level":2,"score":0.5568000078201294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5486000180244446},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5145000219345093},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5133000016212463},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.4986000061035156},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4916999936103821},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.4636000096797943},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4487999975681305},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.4293000102043152},{"id":"https://openalex.org/C176321772","wikidata":"https://www.wikidata.org/wiki/Q1430640","display_name":"Numerical stability","level":3,"score":0.40070000290870667},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.36570000648498535},{"id":"https://openalex.org/C52740198","wikidata":"https://www.wikidata.org/wiki/Q1539564","display_name":"Importance sampling","level":3,"score":0.35910001397132874},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.3544999957084656},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.34450000524520874},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.3370000123977661},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C2776207758","wikidata":"https://www.wikidata.org/wiki/Q5303302","display_name":"Downstream (manufacturing)","level":2,"score":0.3147999942302704},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.30630001425743103},{"id":"https://openalex.org/C151201525","wikidata":"https://www.wikidata.org/wiki/Q177239","display_name":"Limit (mathematics)","level":2,"score":0.2937999963760376},{"id":"https://openalex.org/C2781395549","wikidata":"https://www.wikidata.org/wiki/Q4680762","display_name":"Adaptive sampling","level":3,"score":0.2766000032424927},{"id":"https://openalex.org/C48753275","wikidata":"https://www.wikidata.org/wiki/Q11216","display_name":"Numerical analysis","level":2,"score":0.26750001311302185},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C971699","wikidata":"https://www.wikidata.org/wiki/Q1132714","display_name":"Delta method","level":3,"score":0.2660999894142151},{"id":"https://openalex.org/C45357846","wikidata":"https://www.wikidata.org/wiki/Q2001982","display_name":"Notation","level":2,"score":0.2567000091075897}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.23720855","doi":"https://doi.org/10.5281/zenodo.23720855","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23720855","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.23720856","doi":"https://doi.org/10.5281/zenodo.23720856","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23720856","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.23720855","doi":"https://doi.org/10.5281/zenodo.23720855","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.23720855","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7224081781","https://openalex.org/W7224135969","https://openalex.org/W7223570154","https://openalex.org/W7224121502","https://openalex.org/W7224120613","https://openalex.org/W7224103118","https://openalex.org/W7224120417","https://openalex.org/W7224121078","https://openalex.org/W7224114946","https://openalex.org/W7224116079"],"abstract_inverted_index":{"Diffusion":[0],"models":[1],"commonly":[2],"represent":[3],"their":[4,27,123],"denoising":[5,127],"predictions":[6],"using":[7],"noise":[8,195,210],"prediction,":[9,11,13,196,198,201],"clean-sample":[10,197],"velocity":[12,200],"or":[14,42],"related":[15],"parameterizations.":[16],"Although":[17],"these":[18],"representations":[19,97],"can":[20,168,240],"be":[21],"mathematically":[22,94],"equivalent":[23],"under":[24],"appropriate":[25],"conditions,":[26],"numerical":[28,74,140,206,253],"behavior":[29],"may":[30],"differ":[31],"when":[32],"implemented":[33],"with":[34,45,202],"finite":[35],"precision,":[36],"approximate":[37],"model":[38],"outputs,":[39],"imperfect":[40],"conversions,":[41],"sampling":[43,112],"procedures":[44],"different":[46,153],"stability":[47,207],"properties.":[48],"Conventional":[49],"inference":[50,181],"pipelines":[51],"typically":[52],"employ":[53],"a":[54,59,84,116,134,228],"predetermined":[55],"prediction":[56,69,96,154,239],"parameterization":[57,120],"and":[58,73,98,128,132,142,156,183,199,216],"fixed":[60],"conversion":[61],"pathway,":[62],"while":[63],"existing":[64],"research":[65,230],"has":[66],"investigated":[67],"alternative":[68],"targets,":[70],"training":[71],"objectives,":[72],"formulations.":[75],"This":[76],"paper":[77],"proposes":[78],"Lookahead":[79],"Prediction-Parameterization":[80],"Intervention":[81],"Optimization":[82],"(LPPIO),":[83],"conceptual":[85],"framework":[86,146],"for":[87],"predicting":[88],"the":[89,100,106,162],"downstream":[90,238],"effects":[91,124],"of":[92,118],"alternative,":[93],"compatible":[95,245],"selecting":[99],"computational":[101,143,246],"pathway":[102,135],"expected":[103],"to":[104,137,205],"produce":[105],"best":[107],"generation":[108],"outcome.":[109],"At":[110],"each":[111],"step,":[113],"LPPIO":[114,224],"constructs":[115],"set":[117],"valid":[119],"pathways,":[121],"estimates":[122],"on":[125,236],"subsequent":[126],"final":[129],"sample":[130],"quality,":[131],"selects":[133],"according":[136],"predicted":[138],"utility,":[139],"risk,":[141],"cost.":[144],"The":[145,188],"distinguishes":[147],"exact":[148],"algebraic":[149],"reparameterization":[150],"from":[151,157],"genuinely":[152],"estimators":[155],"implementation-level":[158],"alternatives,":[159],"thereby":[160],"avoiding":[161],"assumption":[163],"that":[164],"changing":[165],"notation":[166],"alone":[167],"improve":[169],"generation.":[170],"A":[171],"mathematical":[172],"formulation,":[173],"parameterization-conversion":[174],"rules,":[175],"lookahead":[176],"evaluator,":[177],"adaptive":[178],"decision":[179],"mechanism,":[180],"algorithm,":[182],"experimental":[184,220],"protocol":[185],"are":[186,222],"presented.":[187],"proposed":[189],"evaluation":[190],"emphasizes":[191],"controlled":[192],"comparisons":[193],"among":[194],"particular":[203],"attention":[204],"near":[208],"limiting":[209],"levels,":[211],"error":[212],"propagation,":[213],"solver":[214],"compatibility,":[215],"equal-computation":[217],"comparisons.":[218],"No":[219],"results":[221],"claimed.":[223],"is":[225],"presented":[226],"as":[227],"testable":[229],"hypothesis":[231],"whose":[232],"practical":[233],"value":[234],"depends":[235],"whether":[237],"identify":[241],"meaningful":[242],"differences":[243],"between":[244],"pathways":[247],"beyond":[248],"those":[249],"explained":[250],"by":[251],"ordinary":[252],"implementation.":[254]},"counts_by_year":[],"updated_date":"2026-10-30T12:39:25.321706","created_date":"2026-10-29T00:00:00"}