1 code implementation • 3 Feb 2024 • Guang-Yuan Hao, Hengguan Huang, Haotian Wang, Jie Gao, Hao Wang
In this paper, we propose the first general method, dubbed composite active learning (CAL), for multi-domain AL. Our approach explicitly considers the domain-level and instance-level information in the problem; CAL first assigns domain-level budgets according to domain-level importance, which is estimated by optimizing an upper error bound that we develop; with the domain-level budgets, CAL then leverages a certain instance-level query strategy to select samples to label from each domain.
no code implementations • 2 Feb 2024 • Guang-Yuan Hao, Jiji Zhang, Biwei Huang, Hao Wang, Kun Zhang
Counterfactual reasoning is pivotal in human cognition and especially important for providing explanations and making decisions.
2 code implementations • 13 Jun 2023 • Tianyi Liu, Zihao Xu, Hao He, Guang-Yuan Hao, Guang-He Lee, Hao Wang
Domain adaptation aims to mitigate distribution shifts among different domains.
4 code implementations • 6 Feb 2023 • Zihao Xu, Guang-Yuan Hao, Hao He, Hao Wang
To address this challenge, we first provide a formal definition of domain index from the probabilistic perspective, and then propose an adversarial variational Bayesian framework that infers domain indices from multi-domain data, thereby providing additional insight on domain relations and improving domain adaptation performance.
1 code implementation • EMNLP 2021 • Baojun Wang, Zhao Zhang, Kun Xu, Guang-Yuan Hao, Yuyang Zhang, Lifeng Shang, Linlin Li, Xiao Chen, Xin Jiang, Qun Liu
Incorporating lexical knowledge into deep learning models has been proved to be very effective for sequence labeling tasks.
no code implementations • 30 Sep 2019 • Guang-Yuan Hao, Hong-Xing Yu, Wei-Shi Zheng
We focus on explicitly learning disentangled representation for natural image generation, where the underlying spatial structure and the rendering on the structure can be independently controlled respectively, yet using no tuple supervision.
1 code implementation • 4 Jul 2018 • Guang-Yuan Hao, Hong-Xing Yu, Wei-Shi Zheng
In this work, we present an interesting attempt on mixture generation: absorbing different image concepts (e. g., content and style) from different domains and thus generating a new domain with learned concepts.