Publications

Gao Cong, Jingyi Yang, Yue Zhao (2024). Machine Learning for Databases: Foundations, Paradigms, and Open problems. SIGMOD 2024.

Yue Zhao, Zhaodonghui Li, Gao Cong (2024). A Comparative Study and Component Analysis of Query Plan Representation Techniques in ML4DB Studies. VLDB 2024.

Zizhong Meng, Xin Cao, Gao Cong (2023). Selectivity Estimation for Queries Containing Predicates over Set-Valued Attributes. SIGMOD 2024.

Jingyi Yang, Gao Cong (2023). PLATON: Top-down R-tree Packing with Learned Partition Policy. SIGMOD 2024.

Song Song Mo, Yile Chen, Hao Wang, Gao Cong, Zhifeng Bao (2023). Lemo: A Cache-Enhanced Learned Optimizer for Concurrent Queries. SIGMOD 2024.

Tu Gu, Kaiyu Feng, Gao Cong, Cheng Long, Zheng Wang, Sheng Wang (2023). The RLR-Tree: A Reinforcement Learning Based R-Tree for Spatial Data. SIGMOD 2023.

Jiangneng Li, Zheng Wang, Gao Cong, Cheng Long, Han Mao Kiah, Bin Cui (2023). Towards Designing and Learning Piecewise Space-Filling Curves. VLDB 2023.

Jiachen Shi, Gao Cong, Xiaoli Li (2023). Learned Index Benefits: Machine Learning Based Index Performance Estimation. VLDB 2023.

Jingyi Yang, Peizhi Wu, Gao Cong, Tieying Zhang, Xiao He (2022). SAM: Database Generation from Query Workloads with Supervised Autoregressive Models. SIGMOD 2022.

Zizhong Meng, Peizhi Wu, Gao Cong, Rong Zhu, Shuai Ma (2022). Unsupervised Selectivity Estimation by Integrating Gaussian Mixture Models and an Autoregressive Model. EDBT 2022.

Yue Zhao, Gao Cong, Jiachen Shi, Chunyan Miao (2022). QueryFormer: a tree transformer model for query plan representation. VLDB 2022.

Peizhi Wu, Gao Cong (2021). A Unified Deep Model of Learning from both Data and Queries for Cardinality Estimation. SIGMOD 2021.