65 citations · 249 across the 18 of their papers we have counts for
9 papers · 1 filter
Graph Mixture of Experts: Learning on Large-Scale Graphs with Explicit Diversity Modeling
Haotao Wang, Ziyu Jiang, Yuning You +5
Graph neural networks (GNNs) have found extensive applications in learning from graph data. However, real-world graphs often possess diverse structures and comprise nodes and edges…
How Robust is Your Fairness? Evaluating and Sustaining Fairness under Unseen Distribution Shifts
Haotao Wang, Junyuan Hong, Jiayu Zhou +1
Increasing concerns have been raised on deep learning fairness in recent years. Existing fairness-aware machine learning methods mainly focus on the fairness of in-distribution dat…
Removing Batch Normalization Boosts Adversarial Training
Haotao Wang, Aston Zhang, Shuai Zheng +3
Adversarial training (AT) defends deep neural networks against adversarial attacks. One challenge that limits its practical application is the performance degradation on clean samp…
Efficient Split-Mix Federated Learning for On-Demand and In-Situ Customization
Junyuan Hong, Haotao Wang, Zhangyang Wang +1
Federated learning (FL) provides a distributed learning framework for multiple participants to collaborate learning without sharing raw data. In many practical FL scenarios, partic…
Federated Robustness Propagation: Sharing Robustness in Heterogeneous Federated Learning
Junyuan Hong, Haotao Wang, Zhangyang Wang +1
Federated learning (FL) emerges as a popular distributed learning schema that learns a model from a set of participating users without sharing raw data. One major challenge of FL c…
Taxonomy of Machine Learning Safety: A Survey and Primer
Sina Mohseni, Haotao Wang, Zhiding Yu +3
The open-world deployment of Machine Learning (ML) algorithms in safety-critical applications such as autonomous vehicles needs to address a variety of ML vulnerabilities such as i…
GAN Slimming: All-in-One GAN Compression by A Unified Optimization Framework
Haotao Wang, Shupeng Gui, Haichuan Yang +2
Generative adversarial networks (GANs) have gained increasing popularity in various computer vision applications, and recently start to be deployed to resource-constrained mobile d…
I Am Going MAD: Maximum Discrepancy Competition for Comparing Classifiers Adaptively
Haotao Wang, Tianlong Chen, Zhangyang Wang +1
The learning of hierarchical representations for image classification has experienced an impressive series of successes due in part to the availability of large-scale labeled data…
Model Compression with Adversarial Robustness: A Unified Optimization Framework
Shupeng Gui, Haotao Wang, Chen Yu +3
Deep model compression has been extensively studied, and state-of-the-art methods can now achieve high compression ratios with minimal accuracy loss. This paper studies model compr…