3 papers
stat.ME2024
A Balanced Statistical Boosting Approach for GAMLSS via New Step Lengths
Alexandra Daub, Andreas Mayr, Boyao Zhang +1
Component-wise gradient boosting algorithms are popular for their intrinsic variable selection and implicit regularization, which can be especially beneficial for very flexible mod…
cs.CL2023
Topics in the Haystack: Extracting and Evaluating Topics beyond Coherence
Anton Thielmann, Quentin Seifert, Arik Reuter +2
Extracting and identifying latent topics in large text corpora has gained increasing importance in Natural Language Processing (NLP). Most models, whether probabilistic models simi…
stat.ME2023
Prediction-based Variable Selection for Component-wise Gradient Boosting
Sophie Potts, Elisabeth Bergherr, Constantin Reinke +1
Model-based component-wise gradient boosting is a popular tool for data-driven variable selection. In order to improve its prediction and selection qualities even further, several…