56 citations · 312 across the 24 of their papers we have counts for
19 papers · 1 filter
OpenBox: A Python Toolkit for Generalized Black-box Optimization
Huaijun Jiang, Yu Shen, Yang Li +5
Black-box optimization (BBO) has a broad range of applications, including automatic machine learning, experimental design, and database knob tuning. However, users still face chall…
Transfer Learning for Bayesian Optimization: A Survey
Tianyi Bai, Yang Li, Yu Shen +3
A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization. Bayesian optimizati…
DivBO: Diversity-aware CASH for Ensemble Learning
Yu Shen, Yupeng Lu, Yang Li +3
The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success…
Rover: An online Spark SQL tuning service via generalized transfer learning
Yu Shen, Xinyuyang Ren, Yupeng Lu +6
Distributed data analytic engines like Spark are common choices to process massive data in industry. However, the performance of Spark SQL highly depends on the choice of configura…
Efficient End-to-End AutoML via Scalable Search Space Decomposition
Yang Li, Yu Shen, Wentao Zhang +2
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, algorit…
NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learning
Wentao Zhang, Zeang Sheng, Mingyu Yang +4
Recently, graph neural networks (GNNs) have shown prominent performance in graph representation learning by leveraging knowledge from both graph structure and node features. Howeve…
DFG-NAS: Deep and Flexible Graph Neural Architecture Search
Wentao Zhang, Zheyu Lin, Yu Shen +3
Graph neural networks (GNNs) have been intensively applied to various graph-based applications. Despite their success, manually designing the well-behaved GNNs requires immense hum…
TransBO: Hyperparameter Optimization via Two-Phase Transfer Learning
Yang Li, Yu Shen, Huaijun Jiang +4
With the extensive applications of machine learning models, automatic hyperparameter optimization (HPO) has become increasingly important. Motivated by the tuning behaviors of huma…
Transfer Learning based Search Space Design for Hyperparameter Tuning
Yang Li, Yu Shen, Huaijun Jiang +4
The tuning of hyperparameters becomes increasingly important as machine learning (ML) models have been extensively applied in data mining applications. Among various approaches, Ba…
PaSca: a Graph Neural Architecture Search System under the Scalable Paradigm
Wentao Zhang, Yu Shen, Zheyu Lin +6
Graph neural networks (GNNs) have achieved state-of-the-art performance in various graph-based tasks. However, as mainstream GNNs are designed based on the neural message passing m…
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…
ProxyBO: Accelerating Neural Architecture Search via Bayesian Optimization with Zero-cost Proxies
Yu Shen, Yang Li, Jian Zheng +6
Designing neural architectures requires immense manual efforts. This has promoted the development of neural architecture search (NAS) to automate the design. While previous NAS met…
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…
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…
GMLP: Building Scalable and Flexible Graph Neural Networks with Feature-Message Passing
Wentao Zhang, Yu Shen, Zheyu Lin +6
In recent studies, neural message passing has proved to be an effective way to design graph neural networks (GNNs), which have achieved state-of-the-art performance in many graph-b…
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…