Zibin Zheng

LayerToFair: An Efficient Post-processing Framework for Layer-aware Fairness Repair of Deep Neural Networks

DNNs are increasingly deployed in high-stakes information systems, where fairness is a critical requirement. Existing post-processing methods can repair unfairness without retraining or accessing original data, but they often treat all layers indiscriminately, resulting in high computational cost and suboptimal effecti...

Panorama: Unveiling Latent Dependencies for Microservice Autoscaling via Meta-learning

One For All: Achieving Adaptive Graph Neural Networks via Mixture of Message Passing

The message passing mechanism, which updates node representations by exchanging messages with their neighbors, plays a critical role in graph neural networks (GNNs) for capturing structural patterns. Since the single message passing mechanism lacks the flexibility to handle graphs with differing node feature quality, G...

One For All: Achieving Adaptive Graph Neural Networks via Mixture of Message Passing

This work proposes Mixture of Message Passing (MoMP), a novel mechanism that incorporates the Mixture of Experts (MoE) paradigm directly into the message passing mechanism of GNNs, treating different message passing mechanisms as ''experts''.