Tootfinder

A model-free subdata selection method for classification
Rakhi Singh
https://arxiv.org/abs/2404.19127 https://arxiv.org/pdf/2404.1912…

A model-free subdata selection method for classification
Subdata selection is a study of methods that select a small representative sample of the big data, the analysis of which is fast and statistically efficient. The existing subdata selection methods assume that the big data can be reasonably modeled using an underlying model, such as a (multinomial) logistic regression for classification problems. These methods work extremely well when the underlying modeling assumption is correct but often yield poor results otherwise. In this paper, we propose …

This https://arxiv.org/abs/2303.16971 has been replaced.
initial toot: https://mastoxiv.page/@arXiv_sta…

Sparse joint shift in multinomial classification
Sparse joint shift (SJS) was recently proposed as a tractable model for general dataset shift which may cause changes to the marginal distributions of features and labels as well as the posterior probabilities and the class-conditional feature distributions. Fitting SJS for a target dataset without label observations may produce valid predictions of labels and estimates of class prior probabilities. We present new results on the transmission of SJS from sets of features to larger sets of featur…