126 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- Huawei Technologies (China)CN21 papers
- Peking UniversityCN10 papers
- Huawei Technologies (Sweden)SE7 papers
- Huawei Technologies (United States)US6 papers
- University College LondonGB5 papers
- University of Hong KongHK5 papers
- Hong Kong University of Science and TechnologyHK3 papers
- Lomonosov Moscow State UniversityRU3 papers
- National Research University Higher School of EconomicsRU3 papers
- Shanghai Jiao Tong UniversityCN3 papers
- Southern University of Science and TechnologyCN3 papers
- Tsinghua UniversityCN3 papers
6 papers · 2 filters
Online Markov Decision Processes with Non-oblivious Strategic Adversary
Le Cong Dinh, David Henry Mguni, Long Tran-Thanh +2
We study a novel setting in Online Markov Decision Processes (OMDPs) where the loss function is chosen by a non-oblivious strategic adversary who follows a no-external regret algor…
IGNNITION: Bridging the Gap Between Graph Neural Networks and Networking Systems
David Pujol-Perich, José Suárez-Varela, Miquel Ferriol +4
Recent years have seen the vast potential of Graph Neural Networks (GNN) in many fields where data is structured as graphs (e.g., chemistry, recommender systems). In particular, GN…
FixNorm: Dissecting Weight Decay for Training Deep Neural Networks
Yucong Zhou, Yunxiao Sun, Zhao Zhong
Weight decay is a widely used technique for training Deep Neural Networks(DNN). It greatly affects generalization performance but the underlying mechanisms are not fully understood…
Reweighting Augmented Samples by Minimizing the Maximal Expected Loss
Mingyang Yi, Lu Hou, Lifeng Shang +3
Data augmentation is an effective technique to improve the generalization of deep neural networks. However, previous data augmentation methods usually treat the augmented samples e…
Improving Hierarchical Adversarial Robustness of Deep Neural Networks
Avery Ma, Aladin Virmaux, Kevin Scaman +1
Do all adversarial examples have the same consequences? An autonomous driving system misclassifying a pedestrian as a car may induce a far more dangerous -- and even potentially le…
Out-of-Distribution Generalization Analysis via Influence Function
Haotian Ye, Chuanlong Xie, Yue Liu +1
The mismatch between training and target data is one major challenge for current machine learning systems. When training data is collected from multiple domains and the target doma…