{"id":"https://openalex.org/W7164038379","doi":"https://doi.org/10.1145/3748522.3780034","title":"From Loss Landscapes to Explanation Robustness: Linking Parameter-Space Geometry to the Stability and Utility of Post-Hoc Explanations","display_name":"From Loss Landscapes to Explanation Robustness: Linking Parameter-Space Geometry to the Stability and Utility of Post-Hoc Explanations","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7164038379","doi":"https://doi.org/10.1145/3748522.3780034"},"language":null,"primary_location":{"id":"doi:10.1145/3748522.3780034","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3780034","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3748522.3780034","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5021377903","display_name":"Iv\u00e1n Rivero","orcid":"https://orcid.org/0009-0006-0466-3065"},"institutions":[{"id":"https://openalex.org/I13134134","display_name":"Universidad de Cantabria","ror":"https://ror.org/046ffzj20","country_code":"ES","type":"education","lineage":["https://openalex.org/I13134134"]}],"countries":["ES"],"is_corresponding":true,"raw_author_name":"Iv\u00e1n Rivero","raw_affiliation_strings":["Matem\u00e1ticas, estad\u00edstica y computaci\u00f3n, Universidad de Cantabria, Santander, Spain"],"raw_orcid":"https://orcid.org/0009-0006-0466-3065","affiliations":[{"raw_affiliation_string":"Matem\u00e1ticas, estad\u00edstica y computaci\u00f3n, Universidad de Cantabria, Santander, Spain","institution_ids":["https://openalex.org/I13134134"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5021377903"],"corresponding_institution_ids":["https://openalex.org/I13134134"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.96674137,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1563","last_page":"1565"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8636000156402588,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.8636000156402588,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.09730000048875809,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.004999999888241291,"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/hessian-matrix","display_name":"Hessian matrix","score":0.6775000095367432},{"id":"https://openalex.org/keywords/curvature","display_name":"Curvature","score":0.6355999708175659},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.5855000019073486},{"id":"https://openalex.org/keywords/maxima-and-minima","display_name":"Maxima and minima","score":0.5766000151634216},{"id":"https://openalex.org/keywords/isotropy","display_name":"Isotropy","score":0.49129998683929443},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.44350001215934753},{"id":"https://openalex.org/keywords/bridging","display_name":"Bridging (networking)","score":0.39980000257492065}],"concepts":[{"id":"https://openalex.org/C203616005","wikidata":"https://www.wikidata.org/wiki/Q620495","display_name":"Hessian matrix","level":2,"score":0.6775000095367432},{"id":"https://openalex.org/C195065555","wikidata":"https://www.wikidata.org/wiki/Q214881","display_name":"Curvature","level":2,"score":0.6355999708175659},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.5855000019073486},{"id":"https://openalex.org/C186633575","wikidata":"https://www.wikidata.org/wiki/Q845060","display_name":"Maxima and minima","level":2,"score":0.5766000151634216},{"id":"https://openalex.org/C184050105","wikidata":"https://www.wikidata.org/wiki/Q273163","display_name":"Isotropy","level":2,"score":0.49129998683929443},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.46630001068115234},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.44350001215934753},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.41119998693466187},{"id":"https://openalex.org/C174348530","wikidata":"https://www.wikidata.org/wiki/Q188635","display_name":"Bridging (networking)","level":2,"score":0.39980000257492065},{"id":"https://openalex.org/C28719098","wikidata":"https://www.wikidata.org/wiki/Q44946","display_name":"Point (geometry)","level":2,"score":0.3950999975204468},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3774999976158142},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3490000069141388},{"id":"https://openalex.org/C85725439","wikidata":"https://www.wikidata.org/wiki/Q466686","display_name":"Anisotropy","level":2,"score":0.32350000739097595},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.31119999289512634},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3068000078201294},{"id":"https://openalex.org/C73586568","wikidata":"https://www.wikidata.org/wiki/Q2600211","display_name":"Parameter space","level":2,"score":0.3009999990463257},{"id":"https://openalex.org/C18762648","wikidata":"https://www.wikidata.org/wiki/Q42213","display_name":"Work (physics)","level":2,"score":0.2976999878883362},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.27410000562667847},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.27070000767707825},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.25369998812675476}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3748522.3780034","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3780034","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3748522.3780034","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3748522.3780034","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 41st ACM/SIGAPP Symposium on Applied Computing","raw_type":"proceedings-article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W2516809705"],"related_works":[],"abstract_inverted_index":{"Post-hoc":[0],"explanation":[1,138],"methods":[2,127],"are":[3,91],"widely":[4],"used":[5],"to":[6,48,93,115],"make":[7],"AI":[8],"systems":[9],"more":[10,80,102],"transparent,":[11],"yet":[12],"their":[13,29],"outputs":[14],"often":[15],"vary":[16],"unpredictably":[17],"across":[18],"random":[19],"seeds,":[20],"training":[21,126],"runs":[22],"or":[23,111],"small":[24],"perturbations,":[25],"raising":[26],"concerns":[27],"about":[28],"reliability.":[30],"This":[31],"work":[32],"explores":[33],"a":[34,59],"geometric":[35],"perspective":[36],"on":[37],"this":[38],"problem":[39],"by":[40,72],"linking":[41],"the":[42,45,49,95],"shape":[43],"of":[44,53],"loss":[46,97],"landscape":[47],"stability":[50],"and":[51,86,131,135,155,162],"utility":[52],"post-hoc":[54],"explanations,":[55],"utilizing":[56],"SHAP":[57],"as":[58,108,129],"representative":[60],"framework.":[61],"Across":[62],"standard":[63],"tabular":[64],"datasets,":[65],"we":[66],"find":[67],"that":[68,151],"flatter":[69],"minima":[70],"characterized":[71],"lower":[73],"curvature":[74,134],"in":[75,121],"parameter":[76],"space":[77],"consistently":[78],"yield":[79],"stable":[81],"attributions":[82],"under":[83],"both":[84],"weight":[85],"input":[87],"perturbations.":[88],"When":[89],"perturbations":[90],"calibrated":[92],"induce":[94],"same":[96],"increase,":[98],"explanations":[99],"remain":[100],"markedly":[101],"robust":[103],"along":[104],"structured":[105],"directions,":[106],"such":[107,128],"dominant":[109],"Hessian":[110],"gradient":[112],"axes,":[113],"compared":[114],"isotropic":[116],"noise,":[117],"revealing":[118],"strong":[119],"anisotropy":[120],"explanatory":[122,157],"fragility.":[123],"Moreover,":[124],"geometry-aware":[125],"SAM":[130],"SWA":[132],"reduce":[133],"can":[136],"improve":[137],"consistency":[139],"without":[140],"harming":[141],"predictive":[142],"accuracy.":[143],"These":[144],"results":[145],"point":[146],"toward":[147],"simple":[148],"geometric-based":[149],"diagnostics":[150],"may":[152],"help":[153],"identify":[154],"mitigate":[156],"instability,":[158],"bridging":[159],"optimization":[160],"geometry":[161],"trustworthy":[163],"AI.":[164]},"counts_by_year":[],"updated_date":"2026-06-10T14:10:52.464848","created_date":"2026-06-10T00:00:00"}