Zobrazeno 1 - 10
of 34
pro vyhledávání: '"Mitra, Ritwik"'
In spite of considerable practical importance, current algorithmic fairness literature lacks technical methods to account for underlying geographic dependency while evaluating or mitigating bias issues for spatial data. We initiate the study of bias
Externí odkaz:
http://arxiv.org/abs/2101.01703
Algorithmic decision making has proliferated and now impacts our daily lives in both mundane and consequential ways. Machine learning practitioners make use of a myriad of algorithms for predictive models in applications as diverse as movie recommend
Externí odkaz:
http://arxiv.org/abs/2008.11249
Autor:
Dodwell, Emily, Flynn, Cheryl, Krishnamurthy, Balachander, Majumdar, Subhabrata, Mitra, Ritwik
Numerous Machine Learning (ML) bias-related failures in recent years have led to scrutiny of how companies incorporate aspects of transparency and accountability in their ML lifecycles. Companies have a responsibility to monitor ML processes for bias
Externí odkaz:
http://arxiv.org/abs/2006.06082
Model selection strategies have been routinely employed to determine a model for data analysis in statistics, and further study and inference then often proceed as though the selected model were the true model that were known a priori. This practice
Externí odkaz:
http://arxiv.org/abs/1802.03511
Autor:
Mitra, Ritwik, Zhang, Cun-Hui
We study confidence regions and approximate chi-squared tests for variable groups in high-dimensional linear regression. When the size of the group is small, low-dimensional projection estimators for individual coefficients can be directly used to co
Externí odkaz:
http://arxiv.org/abs/1412.4170
In this paper, we propose an unsupervised method to identify noun sense changes based on rigorous analysis of time-varying text data available in the form of millions of digitized books. We construct distributional thesauri based networks from data a
Externí odkaz:
http://arxiv.org/abs/1405.4392
Autor:
Mitra, Ritwik, Zhang, Cun-Hui
We study concentration in spectral norm of nonparametric estimates of correlation matrices. We work within the confine of a Gaussian copula model. Two nonparametric estimators of the correlation matrix, the sine transformations of the Kendall's tau a
Externí odkaz:
http://arxiv.org/abs/1403.6195
Publikováno v:
Stat; Dec2023, Vol. 12 Issue 1, p1-13, 13p
Publikováno v:
Journal of the American Statistical Association, 2015 Dec 01. 110(512), 1455-1456.
Externí odkaz:
http://www.jstor.org/stable/24740158
Akademický článek
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