no code implementations • 28 Dec 2023 • Huiling Qin, Xianyuan Zhan, Yuanxun li, Yu Zheng
Jointly solving these two tasks allows full utilization of information from both labeled and unlabeled data, thus alleviating the problem of over-reliance on labeled data.
1 code implementation • 8 Apr 2023 • Fang Wu, Huiling Qin, Siyuan Li, Stan Z. Li, Xianyuan Zhan, Jinbo Xu
In the field of artificial intelligence for science, it is consistently an essential challenge to face a limited amount of labeled data for real-world problems.
2 code implementations • 20 Jul 2022 • Haoran Xu, Xianyuan Zhan, Honglei Yin, Huiling Qin
We study the problem of offline Imitation Learning (IL) where an agent aims to learn an optimal expert behavior policy without additional online environment interactions.
no code implementations • 29 Sep 2021 • Huiling Qin, Xianyuan Zhan, Yuanxun li, Haoran Xu, Yu Zheng
Jointly solving these two tasks allows full utilization of information from both labeled and unlabeled data, thus alleviating the problem of over-reliance on labeled data.
no code implementations • 30 May 2021 • Huiling Qin, Xianyuan Zhan, Yu Zheng
We propose a correlation structure-based collective anomaly detection (CSCAD) model for high-dimensional anomaly detection problem in large systems, which is also generalizable to semi-supervised or supervised settings.