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• W2804162248 abstract "Cross-validation is the workhorse of modern applied statistics and machine learning, as it provides a principled framework for selecting the model that maximizes generalization performance. In this paper, we show that the cross-validation risk is differentiable with respect to the hyperparameters and training data for many common machine learning algorithms, including logistic regression, elastic-net regression, and support vector machines. Leveraging this property of differentiability, we propose a cross-validation gradient method (CVGM) for hyperparameter optimization. Our method enables efficient optimization in high-dimensional hyperparameter spaces of the cross-validation risk, the best surrogate of the true generalization ability of our learning algorithm." @default.
• W2804162248 created "2018-06-01" @default.
• W2804162248 creator A5023055112 @default.
• W2804162248 creator A5059803135 @default.
• W2804162248 date "2018-05-18" @default.
• W2804162248 modified "2023-09-27" @default.
• W2804162248 title "Optimizing for Generalization in Machine Learning with Cross-Validation Gradients." @default.
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• W2804162248 hasPublicationYear "2018" @default.
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