Bayes beats cross validation: fast and accurate ridge regression via expectation maximization
Published in Proceedings of the 36th International Conference on Neural Information Processing Systems (NeurIPS), 2023
Ridge regression is one of the most widely used methods to fit linear regression models. Suprisingly, the established method for estimating its optimal shrinkage hyper-parameter value can be rather inaccurate. We show how a Bayesian formulation can address this issue.
Recommended citation: SY Tew, M Boley, DF Schmidt. (2023). "Bayes beats cross validation: fast and accurate ridge regression via expectation maximization." NeurIPS. 36
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