{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T13:03:31Z","timestamp":1775912611849,"version":"3.50.1"},"reference-count":20,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","issue":"3","license":[{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"am","delay-in-days":0,"URL":"https:\/\/ieeexplore.ieee.org\/Xplorehelp\/downloads\/license-information\/IEEE.html"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2022,5,1]],"date-time":"2022-05-01T00:00:00Z","timestamp":1651363200000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"}],"funder":[{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS 1914489"],"award-info":[{"award-number":["IIS 1914489"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000001","name":"National Science Foundation","doi-asserted-by":"publisher","award":["IIS 2008202"],"award-info":[{"award-number":["IIS 2008202"]}],"id":[{"id":"10.13039\/100000001","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Intell. Syst."],"published-print":{"date-parts":[[2022,5,1]]},"DOI":"10.1109\/mis.2022.3168514","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T19:34:12Z","timestamp":1650396852000},"page":"36-44","source":"Crossref","is-referenced-by-count":3,"title":["Maximizing Fairness in Deep Neural Networks via Mode Connectivity"],"prefix":"10.1109","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1784-0335","authenticated-orcid":false,"given":"Olga","family":"Andreeva","sequence":"first","affiliation":[{"name":"University of Massachusetts Boston, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8298-6023","authenticated-orcid":false,"given":"Matthew","family":"Almeida","sequence":"additional","affiliation":[{"name":"University of Massachusetts Boston, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Wei","family":"Ding","sequence":"additional","affiliation":[{"name":"University of Massachusetts Boston, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Scott E.","family":"Crouter","sequence":"additional","affiliation":[{"name":"The University of Tennessee Knoxville, Knoxville, TN, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3789-7686","authenticated-orcid":false,"given":"Ping","family":"Chen","sequence":"additional","affiliation":[{"name":"University of Massachusetts Boston, Boston, MA, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"263","reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2020.3000681"},{"key":"ref2","first-page":"6373","article-title":"Fairness without harm: Decoupled classifiers with preference guarantees","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Ustun","year":"2019"},{"key":"ref3","first-page":"1929","article-title":"Fairness without demographics in repeated loss minimization","author":"Hashimoto","year":"2018"},{"key":"ref4","first-page":"3992","article-title":"Optimized pre-processing for discrimination prevention","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Calmon","year":"2017"},{"key":"ref5","first-page":"6755","article-title":"Minimax pareto fairness: A multi objective perspective","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Martinez","year":"2020"},{"key":"ref6","first-page":"12091","article-title":"Fairness via representation neutralization","author":"Du","year":"2021","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref7","first-page":"3315","article-title":"Equality of opportunity in supervised learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Hardt","year":"2016"},{"key":"ref8","first-page":"8789","article-title":"Loss surfaces, mode connectivity, and fast ensembling of dnns","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Garipov","year":"2018"},{"key":"ref9","article-title":"Inherent tradeoffs in learning fair representations","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"32","author":"Zhao","year":"2019"},{"key":"ref10","article-title":"Explaining landscape connectivity of low-cost solutions for multilayer nets","volume":"32","author":"Kuditipudi","year":"2019","journal-title":"Proc. Adv. Neural Inf. Process. Syst."},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098095"},{"key":"ref12","article-title":"Toward a better trade-off between performance and fairness with kernel-based distribution matching","author":"Prost","year":"2019"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1145\/2090236.2090255"},{"key":"ref14","first-page":"11214","article-title":"Metric-free individual fairness in online learning","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","volume":"33","author":"Bechavod","year":"2020"},{"key":"ref15","first-page":"2553","article-title":"Asymmetric valleys: Beyond sharp and flat local minima","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"He","year":"2019"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1109\/ITA.2018.8503224"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.1016\/0022-5193(66)90013-0"},{"key":"ref18","first-page":"6781","article-title":"Just train twice: Improving group robustness without training group information","volume-title":"Proc. Int. Conf. Mach. Learn.","author":"Liu","year":"2021"},{"key":"ref19","first-page":"20673","article-title":"Learning from failure: De-biasing classifier from biased classifier","volume-title":"Proc. Adv. Neural Inf. Process. Syst.","author":"Nam","year":"2020"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/ICCV.2019.00153"}],"container-title":["IEEE Intelligent Systems"],"original-title":[],"link":[{"URL":"https:\/\/ieeexplore.ieee.org\/ielam\/9670\/9839421\/9760000-aam.pdf","content-type":"application\/pdf","content-version":"am","intended-application":"syndication"},{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/9670\/9839421\/09760000.pdf?arnumber=9760000","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,15]],"date-time":"2024-05-15T04:49:33Z","timestamp":1715748573000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/9760000\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,5,1]]},"references-count":20,"journal-issue":{"issue":"3"},"URL":"https:\/\/doi.org\/10.1109\/mis.2022.3168514","relation":{},"ISSN":["1541-1672","1941-1294"],"issn-type":[{"value":"1541-1672","type":"print"},{"value":"1941-1294","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,5,1]]}}}