no code implementations • 8 Mar 2024 • Sumin Wang, Chenxian Huang, Yongdao Zhou, Min-Qian Liu
This paper examines an efficient method for quasi-random sampling of copulas in Monte Carlo computations.
no code implementations • 16 Sep 2023 • Liuqing Yang, Yongdao Zhou, Haoda Fu, Min-Qian Liu, Wei Zheng
Specifically, in a $d$-player coalition game, calculating a Shapley value requires the evaluation of $d!$ or $2^d$ marginal contribution values, depending on whether we are taking the permutation or combination formulation of the Shapley value.
no code implementations • 31 Aug 2023 • Siyu Yi, Zhengyang Mao, Wei Ju, Yongdao Zhou, Luchen Liu, Xiao Luo, Ming Zhang
Graph classification, aiming at learning the graph-level representations for effective class assignments, has received outstanding achievements, which heavily relies on high-quality datasets that have balanced class distribution.
no code implementations • 8 Sep 2022 • Mei Zhang, Yongdao Zhou, Zheng Zhou, Aijun Zhang
In order to measure the goodness of representation of a subdata with respect to the original data, we propose a criterion, generalized empirical F-discrepancy (GEFD), and study its theoretical properties in connection with the classical generalized L2-discrepancy in the theory of uniform designs.