Control Flow Operators in PyTorch
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Transductive inference on graphs such as label propagation algorithms is receiving a lot of attention. In this paper, we address a label propagation problem on multiple networks and present a new algorithm that automatically integrates structure information brought in by multiple networks. The proposed method is robust in that irrelevant networks are automatically deemphasized, which is an advantage over Tsuda et al.'s approach (2005). We also show that the proposed algorithm can be interpreted as an expectation-maximization (EM) algorithm with a student-t prior. Finally, we demonstrate the usefulness of our method in protein function prediction and digit classification, and show analytically and experimentally that our algorithm is much more efficient than existing algorithms. © 2008 IEEE.
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Kenneth L. Clarkson, Elad Hazan, et al.
Journal of the ACM
Ankit Vishnubhotla, Charlotte Loh, et al.
NeurIPS 2023
Erik Altman, Jovan Blanusa, et al.
NeurIPS 2023