Consistent Joint Decision-Making with Heterogeneous Learning Models

EACL 2024 This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and compar...

Ausführliche Beschreibung

Gespeichert in:

Bibliographische Detailangaben
Hauptverfasser:	Faghihi, Hossein Rajaby, Kordjamshidi, Parisa
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Learning Computer Science - Logic in Computer Science
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!

container_end_page
container_issue
container_start_page
container_title
container_volume
creator	Faghihi, Hossein Rajaby Kordjamshidi, Parisa
description	EACL 2024 This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.
doi_str_mv	10.48550/arxiv.2402.03728
format	Article
fullrecord	<record><control><sourceid>arxiv_GOX</sourceid><recordid>TN_cdi_arxiv_primary_2402_03728</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2402_03728</sourcerecordid><originalsourceid>FETCH-LOGICAL-a678-99a0dfc01b47a23880b50682e1d3c1352c24f7fc6ca894c002f7b79b764a7c253</originalsourceid><addsrcrecordid>eNotz8tOwzAUBFBvukAtH8CK_EDCjR-xs6zCo6BUbLqPbpzrYtHayA6vv4cWNjOLkUY6jF3VUEmjFNxg-vIfFZfAKxCamwvWdTFkn2cKc_EU_W_ekvXZx1Bu8dWHffHp55diQzOluKdA8T0XPWEKp20bJzrkFVs4PGS6_O8l293f7bpN2T8_PHbrvsRGm7JtESZnoR6lRi6MgVFBYzjVk7C1UNxy6bSzjUXTSgvAnR51O-pGorZciSW7_rs9K4a35I-YvoeTZjhrxA8XVESu</addsrcrecordid><sourcetype>Open Access Repository</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype></control><display><type>article</type><title>Consistent Joint Decision-Making with Heterogeneous Learning Models</title><source>arXiv.org</source><creator>Faghihi, Hossein Rajaby ; Kordjamshidi, Parisa</creator><creatorcontrib>Faghihi, Hossein Rajaby ; Kordjamshidi, Parisa</creatorcontrib><description>EACL 2024 This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.</description><identifier>DOI: 10.48550/arxiv.2402.03728</identifier><language>eng</language><subject>Computer Science - Artificial Intelligence ; Computer Science - Computation and Language ; Computer Science - Learning ; Computer Science - Logic in Computer Science</subject><creationdate>2024-02</creationdate><rights>http://arxiv.org/licenses/nonexclusive-distrib/1.0</rights><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>228,230,776,881</link.rule.ids><linktorsrc>$$Uhttps://arxiv.org/abs/2402.03728$$EView_record_in_Cornell_University$$FView_record_in_$$GCornell_University$$Hfree_for_read</linktorsrc><backlink>$$Uhttps://doi.org/10.48550/arXiv.2402.03728$$DView paper in arXiv$$Hfree_for_read</backlink></links><search><creatorcontrib>Faghihi, Hossein Rajaby</creatorcontrib><creatorcontrib>Kordjamshidi, Parisa</creatorcontrib><title>Consistent Joint Decision-Making with Heterogeneous Learning Models</title><description>EACL 2024 This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.</description><subject>Computer Science - Artificial Intelligence</subject><subject>Computer Science - Computation and Language</subject><subject>Computer Science - Learning</subject><subject>Computer Science - Logic in Computer Science</subject><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>GOX</sourceid><recordid>eNotz8tOwzAUBFBvukAtH8CK_EDCjR-xs6zCo6BUbLqPbpzrYtHayA6vv4cWNjOLkUY6jF3VUEmjFNxg-vIfFZfAKxCamwvWdTFkn2cKc_EU_W_ekvXZx1Bu8dWHffHp55diQzOluKdA8T0XPWEKp20bJzrkFVs4PGS6_O8l293f7bpN2T8_PHbrvsRGm7JtESZnoR6lRi6MgVFBYzjVk7C1UNxy6bSzjUXTSgvAnR51O-pGorZciSW7_rs9K4a35I-YvoeTZjhrxA8XVESu</recordid><startdate>20240206</startdate><enddate>20240206</enddate><creator>Faghihi, Hossein Rajaby</creator><creator>Kordjamshidi, Parisa</creator><scope>AKY</scope><scope>GOX</scope></search><sort><creationdate>20240206</creationdate><title>Consistent Joint Decision-Making with Heterogeneous Learning Models</title><author>Faghihi, Hossein Rajaby ; Kordjamshidi, Parisa</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-a678-99a0dfc01b47a23880b50682e1d3c1352c24f7fc6ca894c002f7b79b764a7c253</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Computer Science - Artificial Intelligence</topic><topic>Computer Science - Computation and Language</topic><topic>Computer Science - Learning</topic><topic>Computer Science - Logic in Computer Science</topic><toplevel>online_resources</toplevel><creatorcontrib>Faghihi, Hossein Rajaby</creatorcontrib><creatorcontrib>Kordjamshidi, Parisa</creatorcontrib><collection>arXiv Computer Science</collection><collection>arXiv.org</collection></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Faghihi, Hossein Rajaby</au><au>Kordjamshidi, Parisa</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Consistent Joint Decision-Making with Heterogeneous Learning Models</atitle><date>2024-02-06</date><risdate>2024</risdate><abstract>EACL 2024 This paper introduces a novel decision-making framework that promotes consistency among decisions made by diverse models while utilizing external knowledge. Leveraging the Integer Linear Programming (ILP) framework, we map predictions from various models into globally normalized and comparable values by incorporating information about decisions' prior probability, confidence (uncertainty), and the models' expected accuracy. Our empirical study demonstrates the superiority of our approach over conventional baselines on multiple datasets.</abstract><doi>10.48550/arxiv.2402.03728</doi><oa>free_for_read</oa></addata></record>
fulltext	fulltext_linktorsrc
identifier	DOI: 10.48550/arxiv.2402.03728
ispartof
issn
language	eng
recordid	cdi_arxiv_primary_2402_03728
source	arXiv.org
subjects	Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Learning Computer Science - Logic in Computer Science
title	Consistent Joint Decision-Making with Heterogeneous Learning Models
url	https://sfx.bib-bvb.de/sfx_tum?ctx_ver=Z39.88-2004&ctx_enc=info:ofi/enc:UTF-8&ctx_tim=2025-02-09T15%3A03%3A20IST&url_ver=Z39.88-2004&url_ctx_fmt=infofi/fmt:kev:mtx:ctx&rfr_id=info:sid/primo.exlibrisgroup.com:primo3-Article-arxiv_GOX&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.genre=article&rft.atitle=Consistent%20Joint%20Decision-Making%20with%20Heterogeneous%20Learning%20Models&rft.au=Faghihi,%20Hossein%20Rajaby&rft.date=2024-02-06&rft_id=info:doi/10.48550/arxiv.2402.03728&rft_dat=%3Carxiv_GOX%3E2402_03728%3C/arxiv_GOX%3E%3Curl%3E%3C/url%3E&disable_directlink=true&sfx.directlink=off&sfx.report_link=0&rft_id=info:oai/&rft_id=info:pmid/&rfr_iscdi=true