402 citations · 843 across the 16 of their papers we have counts for
7 papers · 1 filter
Learning to be a Statistician: Learned Estimator for Number of Distinct Values
Renzhi Wu, Bolin Ding, Xu Chu +4
Estimating the number of distinct values (NDV) in a column is useful for many tasks in database systems, such as columnstore compression and data profiling. In this work, we focus…
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…
Scalable Graph Neural Networks via Bidirectional Propagation
Ming Chen, Zhewei Wei, Bolin Ding +4
Graph Neural Networks (GNN) is an emerging field for learning on non-Euclidean data. Recently, there has been increased interest in designing GNN that scales to large graphs. Most…
Simple and Deep Graph Convolutional Networks
Ming Chen, Zhewei Wei, Zengfeng Huang +2
Graph convolutional networks (GCNs) are a powerful deep learning approach for graph-structured data. Recently, GCNs and subsequent variants have shown superior performance in vario…
Automated Relational Meta-learning
Huaxiu Yao, Xian Wu, Zhiqiang Tao +4
In order to efficiently learn with small amount of data on new tasks, meta-learning transfers knowledge learned from previous tasks to the new ones. However, a critical challenge i…
Continuous Integration of Machine Learning Models with ease.ml/ci: Towards a Rigorous Yet Practical Treatment
Cedric Renggli, Bojan Karlaš, Bolin Ding +4
Continuous integration is an indispensable step of modern software engineering practices to systematically manage the life cycles of system development. Developing a machine learni…