Popular machine learning estimators involve regularization parameters that can be
challenging to tune, and standard strategies rely on grid search for this task. In this paper …

Model selection plays an important role in cost-sensitive SVM (CS-SVM). It has been proven
that the global minimum cross validation (CV) error can be efficiently computed based on the …

Forward stagewise regression follows a very simple strategy for constructing a sequence of
sparse regression estimates: it starts with all coefficients equal to zero, and iteratively …

2 Regularization, Optimization, Kernels, and Support Vector Machines any instance of an l2-
loss soft-margin (or hard-margin) SVM, we construct a Lasso instance having the same …

A new perspective on boosting in linear regression via subgradient optimization and
relatives Page 1 The Annals of Statistics 2017, Vol. 45, No. 6, 2328–2364 https://doi.org/10.1214/16-AOS1505 …

Maximum a posteriori (MAP) estimation, like all Bayesian methods, depends on prior
assumptions. These assumptions are often chosen to promote specific features in the …

In this paper, we consider conditional selective inference (SI) for a linear model estimated
after outliers are removed from the data. To apply the conditional SI framework, it is …

Parameter in learning problems (usually arising from the tradeoff between training error
minimization and regularization) is often tuned by cross validation (CV). A solution path …

If you are predicting the label $ y $ of a new object with $\hat y $, how confident are you that
$ y=\hat y $? Conformal prediction methods provide an elegant framework for answering …

Many modern machine learning algorithms are formulated as regularized M-estimation
problems, in which a regularization (tuning) parameter controls a trade-off between model fit …