47 citations · 187 across the 32 of their papers we have counts for
7 papers · 1 filter
Graph Neural Bandits
Yunzhe Qi, Yikun Ban, Jingrui He
Contextual bandits algorithms aim to choose the optimal arm with the highest reward out of a set of candidates based on the contextual information. Various bandit algorithms have b…
Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey
Dongqi Fu, Wenxuan Bao, Ross Maciejewski +2
In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end…
MLP Fusion: Towards Efficient Fine-tuning of Dense and Mixture-of-Experts Language Models
Mengting Ai, Tianxin Wei, Yifan Chen +2
Fine-tuning a pre-trained language model (PLM) emerges as the predominant strategy in many natural language processing applications. However, this process is known to be expensive,…
Optimizing the Collaboration Structure in Cross-Silo Federated Learning
Wenxuan Bao, Haohan Wang, Jun Wu +1
In federated learning (FL), multiple clients collaborate to train machine learning models together while keeping their data decentralized. Through utilizing more training data, FL…
Neural Exploitation and Exploration of Contextual Bandits
Yikun Ban, Yuchen Yan, Arindam Banerjee +1
In this paper, we study utilizing neural networks for the exploitation and exploration of contextual multi-armed bandits. Contextual multi-armed bandits have been studied for decad…
FairGen: Towards Fair Graph Generation
Lecheng Zheng, Dawei Zhou, Hanghang Tong +3
There have been tremendous efforts over the past decades dedicated to the generation of realistic graphs in a variety of domains, ranging from social networks to computer networks,…
Fairness-aware Multi-view Clustering
Lecheng Zheng, Yada Zhu, Jingrui He
In the era of big data, we are often facing the challenge of data heterogeneity and the lack of label information simultaneously. In the financial domain (e.g., fraud detection), t…