{"id":"https://openalex.org/W7128380299","doi":"https://doi.org/10.48550/arxiv.2602.06858","title":"Designing a Robust, Bounded, and Smooth Loss Function for Improved Supervised Learning","display_name":"Designing a Robust, Bounded, and Smooth Loss Function for Improved Supervised Learning","publication_year":2026,"publication_date":"2026-02-06","ids":{"openalex":"https://openalex.org/W7128380299","doi":"https://doi.org/10.48550/arxiv.2602.06858"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2602.06858","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2602.06858","pdf_url":"https://arxiv.org/pdf/2602.06858","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2602.06858","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5125380904","display_name":"Soumi Mahato","orcid":"https://orcid.org/0009-0008-6513-8218","observed_orcids":["https://orcid.org/0009-0008-6513-8218"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mahato, Soumi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5013989385","display_name":"M. C. Lineesh","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"C, Lineesh M.","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/T12535","display_name":"Machine Learning and Data Classification","score":0.7437000274658203,"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":0.7437000274658203,"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/T10320","display_name":"Neural Networks and Applications","score":0.21699999272823334,"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/T12676","display_name":"Machine Learning and ELM","score":0.007400000002235174,"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/loss-function-design","display_name":"loss function design","score":0.878000020980835},{"id":"https://openalex.org/keywords/outlier-robustness","display_name":"outlier robustness","score":0.6309999823570251},{"id":"https://openalex.org/keywords/high-dimensional-data","display_name":"high-dimensional data","score":0.9169999957084656},{"id":"https://openalex.org/keywords/generalization-analysis","display_name":"generalization analysis","score":0.621999979019165},{"id":"https://openalex.org/keywords/time-series-forecasting","display_name":"time series forecasting","score":0.8640000224113464},{"id":"https://openalex.org/keywords/neural-network-training","display_name":"neural network training","score":0.5899999737739563}],"concepts":[{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6535999774932861},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.638700008392334},{"id":"https://openalex.org/C79337645","wikidata":"https://www.wikidata.org/wiki/Q779824","display_name":"Outlier","level":2,"score":0.6366000175476074},{"id":"https://openalex.org/C14036430","wikidata":"https://www.wikidata.org/wiki/Q3736076","display_name":"Function (biology)","level":2,"score":0.6220999956130981},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.6187000274658203},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.6116999983787537},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6053000092506409},{"id":"https://openalex.org/C2777303404","wikidata":"https://www.wikidata.org/wiki/Q759757","display_name":"Convergence (economics)","level":2,"score":0.5853000283241272},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.555400013923645},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.5239999890327454},{"id":"https://openalex.org/C91873725","wikidata":"https://www.wikidata.org/wiki/Q3445816","display_name":"Function approximation","level":3,"score":0.39239999651908875},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.3589000105857849},{"id":"https://openalex.org/C143724316","wikidata":"https://www.wikidata.org/wiki/Q312468","display_name":"Series (stratigraphy)","level":2,"score":0.33570000529289246},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.30790001153945923},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.29600000381469727},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.29440000653266907},{"id":"https://openalex.org/C117765406","wikidata":"https://www.wikidata.org/wiki/Q5362437","display_name":"Generalization error","level":3,"score":0.27950000762939453},{"id":"https://openalex.org/C58973888","wikidata":"https://www.wikidata.org/wiki/Q1041418","display_name":"Semi-supervised learning","level":2,"score":0.2791999876499176},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.26409998536109924},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26260000467300415},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.2614000141620636}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2602.06858","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2602.06858","pdf_url":"https://arxiv.org/pdf/2602.06858","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2602.06858","doi":"https://doi.org/10.48550/arxiv.2602.06858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.06858","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,"listed_in":[],"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"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2602.06858","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2602.06858","pdf_url":"https://arxiv.org/pdf/2602.06858","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4402673377","https://openalex.org/W4389509958","https://openalex.org/W4366000359","https://openalex.org/W4327992053","https://openalex.org/W4403844223","https://openalex.org/W3179440723","https://openalex.org/W4385644364","https://openalex.org/W3085261469","https://openalex.org/W1969118034","https://openalex.org/W4404498212"],"abstract_inverted_index":{"The":[0,75],"loss":[1,34,68,80,96],"function":[2,69,81],"is":[3,14],"crucial":[4],"to":[5,70,87,105,138],"machine":[6],"learning,":[7],"especially":[8],"in":[9,49,97,144,158],"supervised":[10],"learning":[11,26],"frameworks.":[12],"It":[13],"a":[15,62,101,111],"fundamental":[16],"component":[17],"that":[18,151],"controls":[19],"the":[20,72,79,98,119,140,154],"behavior":[21],"and":[22,43,52,65,109],"general":[23],"efficacy":[24],"of":[25,78,100,121,142,160],"algorithms.":[27],"However,":[28],"despite":[29],"their":[30],"widespread":[31],"use,":[32],"traditional":[33],"functions":[35],"have":[36],"significant":[37],"drawbacks":[38],"when":[39],"dealing":[40],"with":[41],"high-dimensional":[42],"outlier-sensitive":[44],"datasets,":[45],"which":[46],"frequently":[47],"results":[48,149],"reduced":[50],"performance":[51,141],"slower":[53],"convergence":[54],"during":[55],"training.":[56],"In":[57,130],"this":[58],"work,":[59],"we":[60,93,123,132],"develop":[61],"robust,":[63],"bounded,":[64],"smooth":[66],"(RoBoS-NN)":[67],"resolve":[71],"aforementioned":[73],"hindrances.":[74],"generalization":[76],"ability":[77],"has":[82],"also":[83],"been":[84],"theoretically":[85],"analyzed":[86],"rigorously":[88],"justify":[89],"its":[90],"robustness.":[91],"Moreover,":[92],"implement":[94],"RoboS-NN":[95],"framework":[99],"neural":[102],"network":[103],"(NN)":[104],"forecast":[106],"time":[107],"series":[108],"present":[110],"new":[112],"robust":[113],"algorithm":[114],"named":[115],"$\\mathcal{L}_{\\text{RoBoS}}$-NN.":[116],"To":[117],"assess":[118],"potential":[120],"$\\mathcal{L}_{\\text{RoBoS}}$-NN,":[122],"conduct":[124],"experiments":[125],"on":[126],"multiple":[127],"real-world":[128],"datasets.":[129],"addition,":[131],"infuse":[133],"outliers":[134],"into":[135],"data":[136],"sets":[137],"evaluate":[139],"$\\mathcal{L}_{\\text{RoBoS}}$-NN":[143,152],"more":[145],"challenging":[146],"scenarios.":[147],"Numerical":[148],"show":[150],"outperforms":[153],"other":[155],"benchmark":[156],"models":[157],"terms":[159],"accuracy":[161],"measures.":[162]},"counts_by_year":[],"updated_date":"2026-10-28T10:33:24.094632","created_date":"2026-02-10T00:00:00"}
