Category: q-fin.RM
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Holdout cross-validation for large non-Gaussian covariance matrix estimation using Weingarten calculus
Holdout cross-validation for large non-Gaussian covariance matrix estimation using Weingarten calculus arXiv:2509.13923v1 Announce Type: cross Abstract: Cross-validation is one of the most widely used methods for model selection and evaluation; its efficiency for large covariance matrix estimation appears robust in practice, but little is known about the theoretical behavior of its error. In this paper,…
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Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation
Machine Learning with Multitype Protected Attributes: Intersectional Fairness through Regularisation arXiv:2509.08163v1 Announce Type: cross Abstract: Ensuring equitable treatment (fairness) across protected attributes (such as gender or ethnicity) is a critical issue in machine learning. Most existing literature focuses on binary classification, but achieving fairness in regression tasks-such as insurance pricing or hiring score assessments-is equally…
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Discrimination-free Insurance Pricing with Privatized Sensitive Attributes
Discrimination-free Insurance Pricing with Privatized Sensitive Attributes arXiv:2504.11775v1 Announce Type: new Abstract: Fairness has emerged as a critical consideration in the landscape of machine learning algorithms, particularly as AI continues to transform decision-making across societal domains. To ensure that these algorithms are free from bias and do not discriminate against individuals based on sensitive attributes…