D
Depeng Xu
Researcher at University of Arkansas
Publications - 27
Citations - 1030
Depeng Xu is an academic researcher from University of Arkansas. The author has contributed to research in topics: Differential privacy & Computer science. The author has an hindex of 9, co-authored 19 publications receiving 598 citations. Previous affiliations of Depeng Xu include Tsinghua University & Oklahoma Medical Research Foundation.
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Journal ArticleDOI
The microbial gene diversity along an elevation gradient of the Tibetan grassland
Yunfeng Yang,Ying Gao,Shiping Wang,Depeng Xu,Hao Yu,Linwei Wu,Qiaoyan Lin,Yigang Hu,Xiangzhen Li,Zhili He,Ye Deng,Jizhong Zhou +11 more
TL;DR: It is predicted that climate changes in the Tibetan grasslands are very likely to change soil microbial community functional structure, with particular impacts on microbial N-cycling genes and consequently microbe-mediated soil N dynamics.
Journal ArticleDOI
Responses of the functional structure of soil microbial community to livestock grazing in the Tibetan alpine grassland
Yunfeng Yang,Linwei Wu,Qiaoyan Lin,Mengting Yuan,Depeng Xu,Hao Yu,Hao Yu,Hao Yu,Yigang Hu,Jichuang Duan,Xiangzhen Li,Zhili He,Kai Xue,Joy D. Van Nostrand,Shiping Wang,Jizhong Zhou,Jizhong Zhou,Jizhong Zhou +17 more
TL;DR: It is indicated that soil microbial community functional structure was very sensitive to the impact of livestock grazing and revealed microbial functional potentials in regulating soil N and C cycling, supporting the necessity to include microbial components in evaluating the consequence of land-use and/or climate changes.
Proceedings ArticleDOI
FairGAN: Fairness-aware Generative Adversarial Networks
TL;DR: This paper presents fairness-aware generative adversarial networks, called FairGAN, which are able to learn a generator producing fair data and also preserving good data utility, and further ensures the classifiers which are trained on generated data can achieve fair classification on real data.
Posted Content
FairGAN: Fairness-aware Generative Adversarial Networks
TL;DR: FairGAN as discussed by the authors proposes a fairness-aware generative adversarial network (GAN), which is able to learn a generator producing fair data and also preserving good data utility, and further ensures the classifiers which are trained on generated data can achieve fair classification on real data.
Proceedings ArticleDOI
Achieving Causal Fairness through Generative Adversarial Networks.
TL;DR: This paper investigates the problem of building causal fairnessaware generative adversarial networks (CFGAN), which can learn a close distribution from a given dataset, while also ensuring various causal fairness criteria based on a given causal graph.