Our major research focuses on the
- interpretable AI, we are the first to use DRL to understand the environment, [DAFSFluid]https://github.com/G-AILab/DAFSFluid , accpeted by SIGKDD 2022
- Feature selection, identify the most related features for modeling, we are the first to use attention-based solutions for feature selection in tabular data, AAAI 2019 [AFS]https://github.com/G-AILab/AFS, Recently, we proposed the use of self-supervision for semi-supervised feature selection. Source Code to appeared soon.
- Graph Representation Learning, represent complex relations in the graphs without label-guidance. Published in IEEE TKDE, [PairE] https://github.com/G-AILab/PairE
- Oulier Detection, detects the ouliers from normal data, we study the case for tabular data and time series. Source code to be released.
- Data Imputation, fill missing data with self-supervised tasks. Source code to be released upon accptance.
