Results 1 to 10 of about 340,085 (100)
FairLabel: Correcting Bias in Labels
2023 IEEE International Conference on Data Mining Workshops (ICDMW), 2023 There are several algorithms for measuring fairness of ML models. A fundamental assumption in these approaches is that the ground truth is fair or unbiased. In real-world datasets, however, the ground truth often contains data that is a result of historical and societal biases and discrimination.
Srinivasan H. Sengamedu, Hien Pham
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Identification of and correction for publication bias [PDF]
American Economic Review, 2018 Some empirical results are more likely to be published than others. Selective publication leads to biased estimates and distorted inference. We propose two approaches for identifying the conditional probability of publication as a function of a study’s results, the first based on systematic replication studies and the second on meta-studies.
Andrews, Isaiah, Kasy, Maximilian
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Approximate bias correction in econometrics [PDF]
Journal of Econometrics, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
James G. MacKinnon, Anthony A. Smith Jr.
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Bias-Correction In Localization Algorithms [PDF]
GLOBECOM 2009 - 2009 IEEE Global Telecommunications Conference, 2009 In this paper we introduce a new approach to determine the bias in localization algorithms by mixing Taylor series and Jacobian matrices, which results in an easily calculated analytical expression for the bias. To illustrate this approach, we analyze the proposed method in two situations using localization algorithms based on distance measurements ...
Yiming Ji +2 more
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Bias in Error-Corrected Quantum Sensing
Physical Review Letters, 2022 6 + 13 pages, 2 + 8 figures.
Ivan Rojkov +4 more
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Bias loop corrections to the galaxy bispectrum [PDF]
Physical Review D, 2019 Combination of the power spectrum and bispectrum is a powerful way of breaking degeneracies between galaxy bias and cosmological parameters, enabling us to maximize the constraining power from galaxy surveys. Recent cosmological constraints treat the power spectrum and bispectrum on an uneven footing: they include one-loop bias corrections for the ...
Eggemeier, Alexander +2 more
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Bootstrap bias corrections for ensemble methods [PDF]
Statistics and Computing, 2016 This paper examines the use of a residual bootstrap for bias correction in machine learning regression methods. Accounting for bias is an important obstacle in recent efforts to develop statistical inference for machine learning methods. We demonstrate empirically that the proposed bootstrap bias correction can lead to substantial improvements in both ...
Giles Hooker, Lucas Mentch
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N4ITK: Improved N3 Bias Correction [PDF]
IEEE Transactions on Medical Imaging, 2010 A variant of the popular nonparametric nonuniform intensity normalization (N3) algorithm is proposed for bias field correction. Given the superb performance of N3 and its public availability, it has been the subject of several evaluation studies. These studies have demonstrated the importance of certain parameters associated with the B-spline least ...
Nicholas J. Tustison +6 more
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Bias‐Corrected Estimates of GED Returns [PDF]
Journal of Labor Economics, 2006 Using three sources of data, this article examines the direct economic return to General Educational Development (GED) certification for both native and immigrant high school dropouts. One data source—the Current Population Survey (CPS)—is plagued by nonresponse and allocation bias from the hot deck procedure that biases the estimated return to the GED
James J. Heckman, Paul LaFontaine
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Modeling and Correcting Bias in Sequential Evaluation
Proceedings of the 24th ACM Conference on Economics and Computation, 2023 We consider the problem of sequential evaluation, in which an evaluator observes candidates in a sequence and assigns scores to these candidates in an online, irrevocable fashion. Motivated by the psychology literature that has studied sequential bias in such settings -- namely, dependencies between the evaluation outcome and the order in which the ...
Jingyan Wang 0001, Ashwin Pananjady
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