Leave-one-out CV for Lasso
This note is for Homrighausen, D., & McDonald, D. J. (2013). Leave-one-out cross-validation is risk consistent for lasso. ArXiv:1206.6128 [Math, Stat].
Asymptotics of Cross Validation
This note is for Austern, M., & Zhou, W. (2020). Asymptotics of Cross-Validation. ArXiv:2001.11111 [Math, Stat].
Bayesian Leave-One-Out Cross Validation
This note is for Magnusson, M., Andersen, M., Jonasson, J., & Vehtari, A. (2019). Bayesian leave-one-out cross-validation for large data. Proceedings of the 36th International Conference on Machine Learning, 4244–4253.
Bayesian Sparse Multiple Regression
This note is for Chakraborty, A., Bhattacharya, A., & Mallick, B. K. (2020). Bayesian sparse multiple regression for simultaneous rank reduction and variable selection. Biometrika, 107(1), 205–221.
Cross-Validation for High-Dimensional Ridge and Lasso
This note collects several references on the research of cross-validation.
Surprises in High-Dimensional Ridgeless Least Squares Interpolation
This post is based on Hastie, T., Montanari, A., Rosset, S., & Tibshirani, R. J. (2019). Surprises in High-Dimensional Ridgeless Least Squares Interpolation. 53.
Illustrations of Support Vector Machines
Use the e1071 library in R to demonstrate the support vector classifier and the SVM.