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Yang Li
- Author ID
- o0000-0001-5249-1807
- ORCID
-
0000-0001-5249-1807
- Links
-
(found by gpt-5.6-luna on jul 24 2026)
- Most Frequent Institution
- Peking University
- Pagerank
- 0.09403099
- Overall Rank
- 749 | 96.54%
- Paper Count
- 12
Affiliation Timeline
Incoming Non-self Citations Over Time
Total yearly non-self incoming citations across all papers by this author.
Publications by Paper Pagerank
Showing 12 of 12 publications.
| Rank |
Title |
Year |
Venue |
Pagerank |
| 2,772 |
Facilitating Database Tuning with Hyper-Parameter Optimization: A Comprehensive Experimental Evaluation |
2022 |
VLDB |
8.035288e-05 |
| 3,331 |
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition |
2021 |
VLDB |
7.4166095e-05 |
| 3,899 |
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization |
2021 |
VLDB |
6.9358695e-05 |
| 4,079 |
Towards Dynamic and Safe Configuration Tuning for Cloud Databases |
2022 |
SIGMOD |
6.818264e-05 |
| 5,286 |
An Efficient Transfer Learning Based Configuration Adviser for Database Tuning |
2024 |
VLDB |
6.1971399e-05 |
| 5,508 |
SiriusBI: A Comprehensive LLM-Powered Solution for Data Analytics in Business Intelligence |
2025 |
VLDB |
6.0997019e-05 |
| 6,450 |
Towards General and Efficient Online Tuning for Spark |
2023 |
VLDB |
5.7817872e-05 |
| 6,463 |
ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks |
2021 |
SIGMOD |
5.7764694e-05 |
| 6,726 |
A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning |
2023 |
SIGMOD |
5.6948731e-05 |
| 8,277 |
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale |
2022 |
VLDB |
5.3639084e-05 |
| 11,129 |
Oceanus: Enable SLO-Aware Vertical Autoscaling for Cloud-Native Streaming Services in Tencent |
2025 |
SIGMOD |
4.9793485e-05 |
| 11,936 |
Ease.ML: A Lifecycle Management System for MLDev and MLOps |
2021 |
CIDR |
4.9793485e-05 |
Frequent Co-authors
Co-authored at least 5 papers.