24 citations · 62 across the 13 of their papers we have counts for
13 papers
Serverless Federated AUPRC Optimization for Multi-Party Collaborative Imbalanced Data Mining
Xidong Wu, Zhengmian Hu, Jian Pei +1
Multi-party collaborative training, such as distributed learning and federated learning, is used to address the big data challenges. However, traditional multi-party collaborative…
Cooperation or Competition: Avoiding Player Domination for Multi-Target Robustness via Adaptive Budgets
Yimu Wang, Dinghuai Zhang, Yihan Wu +2
Despite incredible advances, deep learning has been shown to be susceptible to adversarial attacks. Numerous approaches have been proposed to train robust networks both empirically…
Decentralized Riemannian Algorithm for Nonconvex Minimax Problems
Xidong Wu, Zhengmian Hu, Heng Huang
The minimax optimization over Riemannian manifolds (possibly nonconvex constraints) has been actively applied to solve many problems, such as robust dimensionality reduction and de…
Communication-Efficient Federated Bilevel Optimization with Local and Global Lower Level Problems
Junyi Li, Feihu Huang, Heng Huang
Bilevel Optimization has witnessed notable progress recently with new emerging efficient algorithms. However, its application in the Federated Learning setting remains relatively u…
Adversarial Weight Perturbation Improves Generalization in Graph Neural Networks
Yihan Wu, Aleksandar Bojchevski, Heng Huang
A lot of theoretical and empirical evidence shows that the flatter local minima tend to improve generalization. Adversarial Weight Perturbation (AWP) is an emerging technique to ef…
Faster Adaptive Federated Learning
Xidong Wu, Feihu Huang, Zhengmian Hu +1
Federated learning has attracted increasing attention with the emergence of distributed data. While extensive federated learning algorithms have been proposed for the non-convex di…
Towards Robust Dataset Learning
Yihan Wu, Xinda Li, Florian Kerschbaum +2
Adversarial training has been actively studied in recent computer vision research to improve the robustness of models. However, due to the huge computational cost of generating adv…
RetrievalGuard: Provably Robust 1-Nearest Neighbor Image Retrieval
Yihan Wu, Hongyang Zhang, Heng Huang
Recent research works have shown that image retrieval models are vulnerable to adversarial attacks, where slightly modified test inputs could lead to problematic retrieval results.…
Communication-Efficient Robust Federated Learning with Noisy Labels
Junyi Li, Jian Pei, Heng Huang
Federated learning (FL) is a promising privacy-preserving machine learning paradigm over distributed located data. In FL, the data is kept locally by each user. This protects the u…
A Law of Robustness beyond Isoperimetry
Yihan Wu, Heng Huang, Hongyang Zhang
We study the robust interpolation problem of arbitrary data distributions supported on a bounded space and propose a two-fold law of robustness. Robust interpolation refers to the…
Decompose to Adapt: Cross-domain Object Detection via Feature Disentanglement
Dongnan Liu, Chaoyi Zhang, Yang Song +4
Recent advances in unsupervised domain adaptation (UDA) techniques have witnessed great success in cross-domain computer vision tasks, enhancing the generalization ability of data-…
Fedlearn-Algo: A flexible open-source privacy-preserving machine learning platform
Bo Liu, Chaowei Tan, Jiazhou Wang +7
In this paper, we present Fedlearn-Algo, an open-source privacy preserving machine learning platform. We use this platform to demonstrate our research and development results on pr…
Data Augmentation for Object Detection via Differentiable Neural Rendering
Guanghan Ning, Guang Chen, Chaowei Tan +3
It is challenging to train a robust object detector under the supervised learning setting when the annotated data are scarce. Thus, previous approaches tackling this problem are in…