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pro vyhledávání: '"Xu, Wenkai"'
Conformal prediction is a non-parametric technique for constructing prediction intervals or sets from arbitrary predictive models under the assumption that the data is exchangeable. It is popular as it comes with theoretical guarantees on the margina
Externí odkaz:
http://arxiv.org/abs/2407.07700
Autor:
Reinert, Gesine, Xu, Wenkai
Generating graphs that preserve characteristic structures while promoting sample diversity can be challenging, especially when the number of graph observations is small. Here, we tackle the problem of graph generation from only one observed graph. Th
Externí odkaz:
http://arxiv.org/abs/2403.18578
Autor:
Shi, Wenqi, Xu, Wenkai
Learning causal relationships is a fundamental problem in science. Anchor regression has been developed to address this problem for a large class of causal graphical models, though the relationships between the variables are assumed to be linear. In
Externí odkaz:
http://arxiv.org/abs/2210.16775
Score-based kernelised Stein discrepancy (KSD) tests have emerged as a powerful tool for the goodness of fit tests, especially in high dimensions; however, the test performance may depend on the choice of kernels in an underlying reproducing kernel H
Externí odkaz:
http://arxiv.org/abs/2210.05746
Autor:
Xu, Wenkai, Reinert, Gesine
Synthetic data generation has become a key ingredient for training machine learning procedures, addressing tasks such as data augmentation, analysing privacy-sensitive data, or visualising representative samples. Assessing the quality of such synthet
Externí odkaz:
http://arxiv.org/abs/2206.00149
Autor:
Xu, Wenkai, Reinert, Gesine
We propose and analyse a novel statistical procedure, coined AgraSSt, to assess the quality of graph generators that may not be available in explicit form. In particular, AgraSSt can be used to determine whether a learnt graph generating process is c
Externí odkaz:
http://arxiv.org/abs/2203.03673
Autor:
Wang, Guangxu, Li, Xin, Yu, Jiaxuan, Xu, Wenkai, Akhter, Muhammad, Ji, Shangyi, Hao, Yinfeng, Li, Daoliang
Publikováno v:
In Computers and Electronics in Agriculture October 2024 225
Autor:
Xu, Wenkai
Non-parametric goodness-of-fit testing procedures based on kernel Stein discrepancies (KSD) are promising approaches to validate general unnormalised distributions in various scenarios. Existing works focused on studying kernel choices to boost test
Externí odkaz:
http://arxiv.org/abs/2106.12105
Modern kernel-based two-sample tests have shown great success in distinguishing complex, high-dimensional distributions with appropriate learned kernels. Previous work has demonstrated that this kernel learning procedure succeeds, assuming a consider
Externí odkaz:
http://arxiv.org/abs/2106.07636
Autor:
Xu, Wenkai, Liu, Chang, Wang, Guangxu, Zhao, Yue, Yu, Jiaxuan, Muhammad, Akhter, Li, Daoliang
Publikováno v:
In Engineering Applications of Artificial Intelligence February 2024 128