Zobrazeno 1 - 10
of 1 653
pro vyhledávání: '"Fairness measure"'
Autor:
Yunyou Huang, Wenjing Liu, Wanling Gao, Xiangjiang Lu, Xiaoshuang Liang, Zhengxin Yang, Hongxiao Li, Li Ma, Suqin Tang
Publikováno v:
BenchCouncil Transactions on Benchmarks, Standards and Evaluations, Vol 3, Iss 3, Pp 100137- (2023)
Algorithmic fairness research is currently receiving significant attention, aiming to ensure that algorithms do not discriminate between different groups or individuals with similar characteristics. However, with the popularization of algorithms in a
Externí odkaz:
https://doaj.org/article/eba324d196484319b48c84eea3b817eb
Autor:
Ridhima Mehta
Publikováno v:
Engineering Reports, Vol 2, Iss 6, Pp n/a-n/a (2020)
Abstract In this article, we propose and formulate a single‐objective nonlinearly constrained programming problem with delay and capacity constraints. Specifically, we consider the utility resource allocation with the goal of maximizing the through
Externí odkaz:
https://doaj.org/article/906ce03f84744c21bcd85b78812b8e57
Autor:
Kentaro Hayakawa, Makoto Ohsaki
Publikováno v:
Journal of the International Association for Shell and Spatial Structures. 62:93-101
A non-parametric approach is proposed for shape design of free-form shells discretized into triangular mesh. The discretized forms of curvatures are used for computing the fairness measures of the surface. The measures are defined as the area of the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::27d0bdbc5055e666cd99c239e0b760fd
https://doi.org/10.20898/j.iass.2021.007
https://doi.org/10.20898/j.iass.2021.007
Autor:
Marc Niethammer, Susan Wei
Publikováno v:
Statistical Analysis and Data Mining: The ASA Data Science Journal. 15:287-302
Mitigating bias in machine learning is a challenging task, due in large part to the presence of competing objectives. Namely, a fair algorithm often comes at the cost of lower predictive accuracy, and vice versa, a highly predictive algorithm may be
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52de7a55c838d0014fb641f37b53930a
https://doi.org/10.1002/sam.11560
https://doi.org/10.1002/sam.11560
Autor:
Mila Hardt, Krishnaram Kenthapadi, Michele Donini, Kevin Haas, Jason Gelman, Pinar Yilmaz, Sanjiv Ranjan Das, Muhammad Bilal Zafar, Pedro Larroy, Jared Katzman
Publikováno v:
The Journal of Financial Data Science. 3:33-64
The authors present a machine learning pipeline for fairness-aware machine learning (FAML) in finance that encompasses metrics for fairness (and accuracy). Whereas accuracy metrics are well understood and the principal ones are used frequently, there
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b26835d478fc932a305db703334643c5
https://doi.org/10.3905/jfds.2021.1.075
https://doi.org/10.3905/jfds.2021.1.075
Publikováno v:
Swami, P, Bhatia, V, Vuppala, S & Ratnarajah, T 2020, ' User Fairness in NOMA-HetNet Using Optimized Power Allocation and Time Slotting ', IEEE Systems Journal . https://doi.org/10.1109/JSYST.2020.2975250
Exponential growth in number of users with diverse data rate requirements has lead to the heterogeneity of traditional cellular networks. To support massive number of users, nonorthogonal multiple access (NOMA) has emerged as a promising solution to
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ba5db6c9482f4fc2e78ca7f25aecd2c2
https://doi.org/10.1109/jsyst.2020.2975250
https://doi.org/10.1109/jsyst.2020.2975250
Publikováno v:
Journal of Economic Behavior & Organization. 183:301-319
Using a theory-driven experiment with working adult participants and deliberate tax framing, this paper reports on the effects of tax withholding choices on subsequent individual income tax reporting behavior. We find reporting increases with the lev
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d8d4968128e5e2b2dbd6a8d9b9df4893
https://doi.org/10.1016/j.jebo.2020.12.030
https://doi.org/10.1016/j.jebo.2020.12.030
Conference
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Publikováno v:
Neurocomputing. 416:231-243
We address the problem of randomized learning and generalization of fair and private classifiers. From one side we want to ensure that sensitive information does not unfairly influence the outcome of a classifier. From the other side we have to learn
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::67da9bba70472894893db0b489614bd9
https://doi.org/10.1016/j.neucom.2019.12.137
https://doi.org/10.1016/j.neucom.2019.12.137
Publikováno v:
Theoretical Computer Science. 818:83-91
In a bipartite graph \(G=(U\cup V,E)\) where \(E \subseteq U \times V\), a semi-matching is defined as a set of edges \(M\subseteq E\), such that each vertex in U is incident with exactly one edge in M. Many previous works focus on the problem of fai
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::02e5b659952f03b32e25ccfb781d863e
https://doi.org/10.1016/j.tcs.2018.03.020
https://doi.org/10.1016/j.tcs.2018.03.020