no code implementations • 9 Aug 2023 • Wenlong Lyu, Shoubo Hu, Jie Chuai, Zhitang Chen
Bayesian optimization (BO) is widely adopted in black-box optimization problems and it relies on a surrogate model to approximate the black-box response function.
1 code implementation • 17 Jun 2022 • Xinwei Shen, Shengyu Zhu, Jiji Zhang, Shoubo Hu, Zhitang Chen
In this paper, we revisit the Greedy Equivalence Search (GES) algorithm, which is widely cited as a score-based algorithm for learning the MEC of the underlying causal structure.
no code implementations • 29 Sep 2021 • Ruichen Luo, Shoubo Hu, Lequan Yu
To this end, we study a new $\textit{selfish}$ variant of federated learning, in which the ultimate objective is to learn a model with optimal performance on internal clients $\textit{alone}$ instead of all clients.
no code implementations • 2 Jun 2021 • Yunqi Wang, Furui Liu, Zhitang Chen, Qing Lian, Shoubo Hu, Jianye Hao, Yik-Chung Wu
Domain generalization aims to learn knowledge invariant across different distributions while semantically meaningful for downstream tasks from multiple source domains, to improve the model's generalization ability on unseen target domains.
no code implementations • 8 Jun 2020 • Vahid Partovi Nia, Xinlin Li, Masoud Asgharian, Shoubo Hu, Zhitang Chen, Yanhui Geng
Our simulation result show that the proposed adjustment significantly improves the performance of the causal direction test statistic for heterogeneous data.
no code implementations • 25 Jul 2019 • Shoubo Hu, Kun Zhang, Zhitang Chen, Laiwan Chan
Domain generalization (DG) aims to incorporate knowledge from multiple source domains into a single model that could generalize well on unseen target domains.
no code implementations • 23 Sep 2018 • Shoubo Hu, Zhitang Chen, Laiwan Chan
Although nonstationary data are more common in the real world, most existing causal discovery methods do not take nonstationarity into consideration.
1 code implementation • NeurIPS 2018 • Shoubo Hu, Zhitang Chen, Vahid Partovi Nia, Laiwan Chan, Yanhui Geng
The inference of the causal relationship between a pair of observed variables is a fundamental problem in science, and most existing approaches are based on one single causal model.