237 citations · 280 across the 8 of their papers we have counts for
8 papers
Evolutionary Architecture Search for Graph Neural Networks
Min Shi, David A. Wilson, Xingquan Zhu +4
Automated machine learning (AutoML) has seen a resurgence in interest with the boom of deep learning over the past decade. In particular, Neural Architecture Search (NAS) has seen…
Multi-Label Graph Convolutional Network Representation Learning
Min Shi, Yufei Tang, Xingquan Zhu +1
Knowledge representation of graph-based systems is fundamental across many disciplines. To date, most existing methods for representation learning primarily focus on networks with…
Feature-Attention Graph Convolutional Networks for Noise Resilient Learning
Min Shi, Yufei Tang, Xingquan Zhu +1
Noise and inconsistency commonly exist in real-world information networks, due to inherent error-prone nature of human or user privacy concerns. To date, tremendous efforts have be…
ClassyTune: A Performance Auto-Tuner for Systems in the Cloud
Yuqing Zhu, Jianxun Liu
Performance tuning can improve the system performance and thus enable the reduction of cloud computing resources needed to support an application. Due to the ever increasing number…
BestConfig: Tapping the Performance Potential of Systems via Automatic Configuration Tuning
Yuqing Zhu, Jianxun Liu, Mengying Guo +5
An ever increasing number of configuration parameters are provided to system users. But many users have used one configuration setting across different workloads, leaving untapped…
ACTS in Need: Automatic Configuration Tuning with Scalability Guarantees
Yuqing Zhu, Jianxun Liu, Mengying Guo +2
To support the variety of Big Data use cases, many Big Data related systems expose a large number of user-specifiable configuration parameters. Highlighted in our experiments, a My…