Groups Don't Move as One: A Bregman Clustering View of Linearized Group Influence
The paper introduces a Bregman clustering view for linearized group influence estimation. It shows that standard methods hide variation by summing individual estimates, whereas using Bregman Information (BI) reveals this hidden dispersion. By decomposing groups into components, the method refines group influence estimates and achieves higher accuracy when compared to standard approaches.