6 citations · 6 across the 7 of their papers we have counts for
7 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…
Auditing Privacy Mechanisms via Label Inference Attacks
Róbert István Busa-Fekete, Travis Dick, Claudio Gentile +3
We propose reconstruction advantage measures to audit label privatization mechanisms. A reconstruction advantage measure quantifies the increase in an attacker's ability to infer t…
Faster Margin Maximization Rates for Generic and Adversarially Robust Optimization Methods
Guanghui Wang, Zihao Hu, Claudio Gentile +2
First-order optimization methods tend to inherently favor certain solutions over others when minimizing an underdetermined training objective that has multiple global optima. This…