Yu Shen

Orcid: 0000-0001-6503-6504

Affiliations:
  • Tencent
  • Peking University, Beijing, China (former)


According to our database1, Yu Shen authored at least 25 papers between 2021 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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PhD thesis 
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Links

Online presence:

On csauthors.net:

Bibliography

2024
NPA: Improving Large-scale Graph Neural Networks with Non-parametric Attention.
Proceedings of the Companion of the 2024 International Conference on Management of Data, 2024

2023
VolcanoML: speeding up end-to-end AutoML via scalable search space decomposition.
VLDB J., March, 2023

Towards General and Efficient Online Tuning for Spark.
Proc. VLDB Endow., 2023

OpenBox: A Python Toolkit for Generalized Black-box Optimization.
CoRR, 2023

Transfer Learning for Bayesian Optimization: A Survey.
CoRR, 2023

Rover: An Online Spark SQL Tuning Service via Generalized Transfer Learning.
Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2023

ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-Cost Proxies.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale.
Proc. VLDB Endow., 2022

Efficient End-to-End AutoML via Scalable Search Space Decomposition.
CoRR, 2022

DFG-NAS: Deep and Flexible Graph Neural Architecture Search.
CoRR, 2022

AutoDC: an automatic machine learning framework for disease classification.
Bioinform., 2022

PaSca: A Graph Neural Architecture Search System under the Scalable Paradigm.
Proceedings of the WWW '22: The ACM Web Conference 2022, Virtual Event, Lyon, France, April 25, 2022

DivBO: Diversity-aware CASH for Ensemble Learning.
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

Transfer Learning based Search Space Design for Hyperparameter Tuning.
Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning.
Proceedings of the International Conference on Machine Learning, 2022

Deep and Flexible Graph Neural Architecture Search.
Proceedings of the International Conference on Machine Learning, 2022

2021
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization.
Proc. VLDB Endow., 2021

VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition.
Proc. VLDB Endow., 2021

Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup.
CoRR, 2021

GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing.
CoRR, 2021

ALG: Fast and Accurate Active Learning Framework for Graph Convolutional Networks.
Proceedings of the SIGMOD '21: International Conference on Management of Data, 2021

ROD: Reception-aware Online Distillation for Sparse Graphs.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

OpenBox: A Generalized Black-box Optimization Service.
Proceedings of the KDD '21: The 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, 2021

MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements.
Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence, 2021


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