46 citations · 129 across the 14 of their papers we have counts for
14 papers
DelvePO: Direction-Guided Self-Evolving Framework for Flexible Prompt Optimization
Tao Tao, Guanghui Zhu, Lang Guo +3
Prompt Optimization has emerged as a crucial approach due to its capabilities in steering Large Language Models to solve various tasks. However, current works mainly rely on the ra…
BOASF: A Unified Framework for Speeding up Automatic Machine Learning via Adaptive Successive Filtering
Guanghui Zhu, Xin Fang, Feng Cheng +4
Machine learning has been making great success in many application areas. However, for the non-expert practitioners, it is always very challenging to address a machine learning tas…
SA-GNAS: Seed Architecture Expansion for Efficient Large-scale Graph Neural Architecture Search
Guanghui Zhu, Zipeng Ji, Jingyan Chen +3
GNAS (Graph Neural Architecture Search) has demonstrated great effectiveness in automatically designing the optimal graph neural architectures for multiple downstream tasks, such a…
Simple and Efficient Partial Graph Adversarial Attack: A New Perspective
Guanghui Zhu, Mengyu Chen, Chunfeng Yuan +1
As the study of graph neural networks becomes more intensive and comprehensive, their robustness and security have received great research interest. The existing global attack meth…
HAGNN: Hybrid Aggregation for Heterogeneous Graph Neural Networks
Guanghui Zhu, Zhennan Zhu, Hongyang Chen +2
Heterogeneous graph neural networks (GNNs) have been successful in handling heterogeneous graphs. In existing heterogeneous GNNs, meta-path plays an essential role. However, recent…
AutoAC: Towards Automated Attribute Completion for Heterogeneous Graph Neural Network
Guanghui Zhu, Zhennan Zhu, Wenjie Wang +3
Many real-world data can be modeled as heterogeneous graphs that contain multiple types of nodes and edges. Meanwhile, due to excellent performance, heterogeneous graph neural netw…
Knowledge-enhanced Black-box Attacks for Recommendations
Jingfan Chen, Wenqi Fan, Guanghui Zhu +4
Recent studies have shown that deep neural networks-based recommender systems are vulnerable to adversarial attacks, where attackers can inject carefully crafted fake user profiles…
Transition Relation Aware Self-Attention for Session-based Recommendation
Guanghui Zhu, Haojun Hou, Jingfan Chen +2
Session-based recommendation is a challenging problem in the real-world scenes, e.g., ecommerce, short video platforms, and music platforms, which aims to predict the next click ac…
UniGPS: A Unified Programming Framework for Distributed Graph Processing
Zhaokang Wang, Junhong Li, Yifan Qi +3
The industry and academia have proposed many distributed graph processing systems. However, the existing systems are not friendly enough for users like data analysts and algorithm…
PDNet: Toward Better One-Stage Object Detection With Prediction Decoupling
Li Yang, Yan Xu, Shaoru Wang +4
Recent one-stage object detectors follow a per-pixel prediction approach that predicts both the object category scores and boundary positions from every single grid location. Howev…
Open-book Video Captioning with Retrieve-Copy-Generate Network
Ziqi Zhang, Zhongang Qi, Chunfeng Yuan +4
Due to the rapid emergence of short videos and the requirement for content understanding and creation, the video captioning task has received increasing attention in recent years.…
DIFER: Differentiable Automated Feature Engineering
Guanghui Zhu, Zhuoer Xu, Xu Guo +2
Feature engineering, a crucial step of machine learning, aims to extract useful features from raw data to improve data quality. In recent years, great efforts have been devoted to…
Distributed Subgraph Enumeration via Backtracking-based Framework
Zhaokang Wang, Weiwei Hu, Chunfeng Yuan +2
Finding or monitoring subgraph instances that are isomorphic to a given pattern graph in a data graph is a fundamental query operation in many graph analytic applications, such as…
Semi-supervised Embedding Learning for High-dimensional Bayesian Optimization
Jingfan Chen, Guanghui Zhu, Chunfeng Yuan +1
Bayesian optimization is a broadly applied methodology to optimize the expensive black-box function. Despite its success, it still faces the challenge from the high-dimensional sea…