47 citations · 187 across the 32 of their papers we have counts for
32 papers
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…
Non-IID Transfer Learning on Graphs
Jun Wu, Jingrui He, Elizabeth Ainsworth
Transfer learning refers to the transfer of knowledge or information from a relevant source domain to a target domain. However, most existing transfer learning theories and algorit…
Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative
Tianxin Wei, Yuning You, Tianlong Chen +3
This paper targets at improving the generalizability of hypergraph neural networks in the low-label regime, through applying the contrastive learning approach from images/graphs (w…
Improved Algorithms for Neural Active Learning
Yikun Ban, Yuheng Zhang, Hanghang Tong +2
We improve the theoretical and empirical performance of neural-network(NN)-based active learning algorithms for the non-parametric streaming setting. In particular, we introduce tw…
MentorGNN: Deriving Curriculum for Pre-Training GNNs
Dawei Zhou, Lecheng Zheng, Dongqi Fu +2
Graph pre-training strategies have been attracting a surge of attention in the graph mining community, due to their flexibility in parameterizing graph neural networks (GNNs) witho…
BOBA: Byzantine-Robust Federated Learning with Label Skewness
Wenxuan Bao, Jun Wu, Jingrui He
In federated learning, most existing robust aggregation rules (AGRs) combat Byzantine attacks in the IID setting, where client data is assumed to be independent and identically dis…
A Unified Meta-Learning Framework for Dynamic Transfer Learning
Jun Wu, Jingrui He
Transfer learning refers to the transfer of knowledge or information from a relevant source task to a target task. However, most existing works assume both tasks are sampled from a…
Privacy-preserving Graph Analytics: Secure Generation and Federated Learning
Dongqi Fu, Jingrui He, Hanghang Tong +1
Directly motivated by security-related applications from the Homeland Security Enterprise, we focus on the privacy-preserving analysis of graph data, which provides the crucial cap…
Fairness-aware Model-agnostic Positive and Unlabeled Learning
Ziwei Wu, Jingrui He
With the increasing application of machine learning in high-stake decision-making problems, potential algorithmic bias towards people from certain social groups poses negative impa…
Neural Bandit with Arm Group Graph
Yunzhe Qi, Yikun Ban, Jingrui He
Contextual bandits aim to identify among a set of arms the optimal one with the highest reward based on their contextual information. Motivated by the fact that the arms usually ex…
Comprehensive Fair Meta-learned Recommender System
Tianxin Wei, Jingrui He
In recommender systems, one common challenge is the cold-start problem, where interactions are very limited for fresh users in the systems. To address this challenge, recently, man…
DISCO: Comprehensive and Explainable Disinformation Detection
Dongqi Fu, Yikun Ban, Hanghang Tong +2
Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous iss…
Neural Collaborative Filtering Bandits via Meta Learning
Yikun Ban, Yunzhe Qi, Tianxin Wei +1
Contextual multi-armed bandits provide powerful tools to solve the exploitation-exploration dilemma in decision making, with direct applications in the personalized recommendation.…
Adaptive Transfer Learning for Plant Phenotyping
Jun Wu, Elizabeth A. Ainsworth, Sheng Wang +2
Plant phenotyping (Guo et al. 2021; Pieruschka et al. 2019) focuses on studying the diverse traits of plants related to the plants' growth. To be more specific, by accurately measu…
FairIF: Boosting Fairness in Deep Learning via Influence Functions with Validation Set Sensitive Attributes
Haonan Wang, Ziwei Wu, Jingrui He
Most fair machine learning methods either highly rely on the sensitive information of the training samples or require a large modification on the target models, which hinders their…
From Intrinsic to Counterfactual: On the Explainability of Contextualized Recommender Systems
Yao Zhou, Haonan Wang, Jingrui He +1
With the prevalence of deep learning based embedding approaches, recommender systems have become a proven and indispensable tool in various information filtering applications. Howe…
Deeper-GXX: Deepening Arbitrary GNNs
Lecheng Zheng, Dongqi Fu, Ross Maciejewski +1
Recently, motivated by real applications, a major research direction in graph neural networks (GNNs) is to explore deeper structures. For instance, the graph connectivity is not al…
Deep Active Learning by Leveraging Training Dynamics
Haonan Wang, Wei Huang, Ziwei Wu +3
Active learning theories and methods have been extensively studied in classical statistical learning settings. However, deep active learning, i.e., active learning with deep learni…