152 citations · 315 across the 17 of their papers we have counts for
11 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 Graph Neural Network Inference at Large Scale
Xinyi Gao, Wentao Zhang, Yingxia Shao +3
Graph neural networks (GNNs) have demonstrated excellent performance in a wide range of applications. However, the enormous size of large-scale graphs hinders their applications un…
Diffusion Models: A Comprehensive Survey of Methods and Applications
Ling Yang, Zhilong Zhang, Yang Song +6
Diffusion models have emerged as a powerful new family of deep generative models with record-breaking performance in many applications, including image synthesis, video generation,…
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…
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…