4 papers
Statistical Learning from Attribution Sets
Lorne Applebaum, Robert Busa-Fekete, August Y. Chen +3
We address the problem of training conversion prediction models in advertising domains under privacy constraints, where direct links between ad clicks and conversions are unavailab…
TBDFiltering: Sample-Efficient Tree-Based Data Filtering
Robert Istvan Busa-Fekete, Julian Zimmert, Anne Xiangyi Zheng +2
The quality of machine learning models depends heavily on their training data. Selecting high-quality, diverse training sets for large language models (LLMs) is a difficult task, d…
Optimal Learning from Label Proportions with General Loss Functions
Lorne Applebaum, Travis Dick, Claudio Gentile +2
Motivated by problems in online advertising, we address the task of Learning from Label Proportions (LLP). We introduce a novel and versatile low-variance debiasing methodology to…
Nearly Optimal Sample Complexity for Learning with Label Proportions
Robert Busa-Fekete, Travis Dick, Claudio Gentile +3
We investigate Learning from Label Proportions (LLP), a partial information setting where examples in a training set are grouped into bags, and only aggregate label values in each…