1 citations · 1 across the 3 of their papers we have counts for
3 papers
cs.LG2026
MBD: A Model-Based Debiasing Framework Across User, Content, and Model Dimensions
Yuantong Li, Lei Yuan, Zhihao Zheng +27
Modern recommendation systems rank candidates by aggregating multiple behavioral signals through a value model. However, many commonly used signals are inherently affected by heter…
stat.ME2026
Fast Uncertainty Quantification for Kernel-Based Estimators in Large-Scale Causal Inference
Matthew Kosko, Falco J, Bargagli-Stoffi +2
Kernel methods are widely used in causal inference for tasks such as treatment effect estimation, policy evaluation, and policy learning. The bootstrap is a standard tool for uncer…
stat.ME2023★ 1 cited
A Fast Bootstrap Algorithm for Causal Inference with Large Data
Matthew Kosko, Lin Wang, Michele Santacatterina
Estimating causal effects from large experimental and observational data has become increasingly prevalent in both industry and research. The bootstrap is an intuitive and powerful…