4 papers
Graph Concept Bottleneck Models
Haotian Xu, Tsui-Wei Weng, Lam M. Nguyen +1
Concept Bottleneck Models (CBMs) provide explicit interpretations for deep neural networks through concepts and allow intervention with concepts to adjust final predictions. Existi…
Online Learning for Autoregressive Multilayer Stochastic Block Models under Stationarity and Non-Stationarity
Fan Wang, Haotian Xu, Yi Yu
Dynamic multilayer networks arise in many applications where multiple types of relations among a common set of nodes evolve over time. Existing approaches often assume temporal ind…
PRIME: Efficient Algorithm for Token Graph Routing Problem
Haotian Xu, Yuqing Zhu, Yuming Huang +1
Optimizing asset exchanges on blockchain-driven platforms poses a novel and challenging graph query optimization problem. In this model, assets represent vertices and exchanges for…
FedFa: A Fully Asynchronous Training Paradigm for Federated Learning
Haotian Xu, Zhaorui Zhang, Sheng Di +3
Federated learning has been identified as an efficient decentralized training paradigm for scaling the machine learning model training on a large number of devices while guaranteei…