FIND: Explainable Framework for Meta-learning. - Xinyue Shao; Hongzhi Wang 001; Xiao Zhu; Feng Xiong (CoRR, 2022)

Auto-CASH: A meta-learning embedding approach for autonomous classification algorithm selection. - Tianyu Mu; Hongzhi Wang 001; Chunnan Wang; Zheng Liang 002; Xinyue Shao (Inf. Sci., 2022)

TSC-AutoML: Meta-learning for Automatic Time Series Classification Algorithm Selection. - Tianyu Mu; Hongzhi Wang 001; Shenghe Zheng; Zhiyu Liang; Chunnan Wang; Xinyue Shao; Zheng Liang 002 (ICDE, 2023)

EFFECT: Explainable framework for meta-learning in automatic classification algorithm selection. - Xinyue Shao; Hongzhi Wang 001; Xiao Zhu; Feng Xiong; Tianyu Mu; Yan Zhang (Inf. Sci., 2023)

ShrinkHPO: Towards Explainable Parallel Hyperparameter Optimization. - Tianyu Mu; Hongzhi Wang 001; Haoyun Tang; Xinyue Shao (ICDE, 2024)

CUBE: Causal Intervention-based Counterfactual Explanation for Prediction Models (Extended Abstract). - Xinyue Shao; Hongzhi Wang 001; Xiang Chen 019; Xiao Zhu; Yan Zhang (ICDE, 2024)

Explainable Database Management System Configuration Tuning through Counterfactuals. - Xinyue Shao; Hongzhi Wang 001; Xiao Zhu; Tianyu Mu; Yan Zhang (ICDE, 2024)

CUBE: Causal Intervention-Based Counterfactual Explanation for Prediction Models. - Xinyue Shao; Hongzhi Wang 001; Xiang Chen 019; Xiao Zhu; Yan Zhang (IEEE Trans. Knowl. Data Eng., 2024)