60 citations · 252 across the 11 of their papers we have counts for
10 papers · 1 filter
Hyper-Tune: Towards Efficient Hyper-parameter Tuning at Scale
Yang Li, Yu Shen, Huaijun Jiang +5
The ever-growing demand and complexity of machine learning are putting pressure on hyper-parameter tuning systems: while the evaluation cost of models continues to increase, the sc…
Automated Hyperparameter Optimization Challenge at CIKM 2021 AnalyticCup
Huaijun Jiang, Yu Shen, Yang Li
In this paper, we describe our method for tackling the automated hyperparameter optimization challenge in QQ Browser 2021 AI Algorithm Competiton (ACM CIKM 2021 AnalyticCup Track 2…
Node Dependent Local Smoothing for Scalable Graph Learning
Wentao Zhang, Mingyu Yang, Zeang Sheng +5
Recent works reveal that feature or label smoothing lies at the core of Graph Neural Networks (GNNs). Concretely, they show feature smoothing combined with simple linear regression…
Grain: Improving Data Efficiency of Graph Neural Networks via Diversified Influence Maximization
Wentao Zhang, Zhi Yang, Yexin Wang +4
Data selection methods, such as active learning and core-set selection, are useful tools for improving the data efficiency of deep learning models on large-scale datasets. However,…
ROD: Reception-aware Online Distillation for Sparse Graphs
Wentao Zhang, Yuezihan Jiang, Yang Li +6
Graph neural networks (GNNs) have been widely used in many graph-based tasks such as node classification, link prediction, and node clustering. However, GNNs gain their performance…
VolcanoML: Speeding up End-to-End AutoML via Scalable Search Space Decomposition
Yang Li, Yu Shen, Wentao Zhang +8
End-to-end AutoML has attracted intensive interests from both academia and industry, which automatically searches for ML pipelines in a space induced by feature engineering, algori…