Safe grid search with optimal complexity
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 …
challenging to tune, and standard strategies rely on grid search for this task. In this paper …
Cross validation through two-dimensional solution surface for cost-sensitive SVM
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 …
that the global minimum cross validation (CV) error can be efficiently computed based on the …
[PDF][PDF] A general framework for fast stagewise algorithms.
RJ Tibshirani - J. Mach. Learn. Res., 2015 - jmlr.org
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 …
sparse regression estimates: it starts with all coefficients equal to zero, and iteratively …
An equivalence between the lasso and support vector machines
M Jaggi - … , optimization, kernels, and support vector machines, 2013 - api.taylorfrancis.com
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 …
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
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 …
relatives Page 1 The Annals of Statistics 2017, Vol. 45, No. 6, 2328–2364 https://doi.org/10.1214/16-AOS1505 …
Path-following methods for Maximum a Posteriori estimators in Bayesian hierarchical models: How estimates depend on hyperparameters
Maximum a posteriori (MAP) estimation, like all Bayesian methods, depends on prior
assumptions. These assumptions are often chosen to promote specific features in the …
assumptions. These assumptions are often chosen to promote specific features in the …
Conditional selective inference for robust regression and outlier detection using piecewise-linear homotopy continuation
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 …
after outliers are removed from the data. To apply the conditional SI framework, it is …
A solution path algorithm for general parametric quadratic programming problem
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 …
minimization and regularization) is often tuned by cross validation (CV). A solution path …
Computing full conformal prediction set with approximate homotopy
E Ndiaye, I Takeuchi - Advances in Neural Information …, 2019 - proceedings.neurips.cc
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 …
$ y=\hat y $? Conformal prediction methods provide an elegant framework for answering …
Path following algorithms for -regularized -estimation with approximation guarantee
Y Zhu, R Liu - Advances in Neural Information Processing …, 2024 - proceedings.neurips.cc
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 …
problems, in which a regularization (tuning) parameter controls a trade-off between model fit …