106 citations · 209 across the 4 of their papers we have counts for
6 papers
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…
OpenBox: A Generalized Black-box Optimization Service
Yang Li, Yu Shen, Wentao Zhang +9
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, engineering, physics, and experimental design. However, it remains a challenge…
Towards Demystifying Serverless Machine Learning Training
Jiawei Jiang, Shaoduo Gan, Yue Liu +6
The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-intensive applications such as ETL, query processing, or machine learning (ML). Several sy…
Efficient Automatic CASH via Rising Bandits
Yang Li, Jiawei Jiang, Jinyang Gao +3
The Combined Algorithm Selection and Hyperparameter optimization (CASH) is one of the most fundamental problems in Automatic Machine Learning (AutoML). The existing Bayesian optimi…
MFES-HB: Efficient Hyperband with Multi-Fidelity Quality Measurements
Yang Li, Yu Shen, Jiawei Jiang +3
Hyperparameter optimization (HPO) is a fundamental problem in automatic machine learning (AutoML). However, due to the expensive evaluation cost of models (e.g., training deep lear…
An Experimental Evaluation of Large Scale GBDT Systems
Fangcheng Fu, Jiawei Jiang, Yingxia Shao +1
Gradient boosting decision tree (GBDT) is a widely-used machine learning algorithm in both data analytic competitions and real-world industrial applications. Further, driven by the…