75 citations · 473 across the 72 of their papers we have counts for
30 papers · 1 filter
Knowledge Distillation on Graphs: A Survey
Yijun Tian, Shichao Pei, Xiangliang Zhang +2
Graph Neural Networks (GNNs) have attracted tremendous attention by demonstrating their capability to handle graph data. However, they are difficult to be deployed in resource-limi…
Towards Efficient and Domain-Agnostic Evasion Attack with High-dimensional Categorical Inputs
Hongyan Bao, Yufei Han, Yujun Zhou +2
Our work targets at searching feasible adversarial perturbation to attack a classifier with high-dimensional categorical inputs in a domain-agnostic setting. This is intrinsically…
NOSMOG: Learning Noise-robust and Structure-aware MLPs on Graphs
Yijun Tian, Chuxu Zhang, Zhichun Guo +2
While Graph Neural Networks (GNNs) have demonstrated their efficacy in dealing with non-Euclidean structural data, they are difficult to be deployed in real applications due to the…
Positive-Unlabeled Learning with Adversarial Data Augmentation for Knowledge Graph Completion
Zhenwei Tang, Shichao Pei, Zhao Zhang +4
Most real-world knowledge graphs (KG) are far from complete and comprehensive. This problem has motivated efforts in predicting the most plausible missing facts to complete a given…
Few-Shot Learning on Graphs
Chuxu Zhang, Kaize Ding, Jundong Li +4
Graph representation learning has attracted tremendous attention due to its remarkable performance in many real-world applications. However, prevailing supervised graph representat…
Meta Cross-Modal Hashing on Long-Tailed Data
Runmin Wang, Guoxian Yu, Carlotta Domeniconi +1
Due to the advantage of reducing storage while speeding up query time on big heterogeneous data, cross-modal hashing has been extensively studied for approximate nearest neighbor s…
Crowdsourcing with Meta-Workers: A New Way to Save the Budget
Guangyang Han, Guoxian Yu, Lizhen Cui +2
Due to the unreliability of Internet workers, it's difficult to complete a crowdsourcing project satisfactorily, especially when the tasks are multiple and the budget is limited. R…
FARF: A Fair and Adaptive Random Forests Classifier
Wenbin Zhang, Albert Bifet, Xiangliang Zhang +2
As Artificial Intelligence (AI) is used in more applications, the need to consider and mitigate biases from the learned models has followed. Most works in developing fair learning…
Attack Transferability Characterization for Adversarially Robust Multi-label Classification
Zhuo Yang, Yufei Han, Xiangliang Zhang
Despite of the pervasive existence of multi-label evasion attack, it is an open yet essential problem to characterize the origin of the adversarial vulnerability of a multi-label l…
Combat Data Shift in Few-shot Learning with Knowledge Graph
Yongchun Zhu, Fuzhen Zhuang, Xiangliang Zhang +4
Many few-shot learning approaches have been designed under the meta-learning framework, which learns from a variety of learning tasks and generalizes to new tasks. These meta-learn…
Characterizing the Evasion Attackability of Multi-label Classifiers
Zhuo Yang, Yufei Han, Xiangliang Zhang
Evasion attack in multi-label learning systems is an interesting, widely witnessed, yet rarely explored research topic. Characterizing the crucial factors determining the attackabi…
Multi-typed Objects Multi-view Multi-instance Multi-label Learning
Yuanlin Yang, Guoxian Yu, Jun Wang +2
Multi-typed objects Multi-view Multi-instance Multi-label Learning (M4L) deals with interconnected multi-typed objects (or bags) that are made of diverse instances, represented wit…
Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation
Uchenna Akujuobi, Jun Chen, Mohamed Elhoseiny +2
Understanding the relationships between biomedical terms like viruses, drugs, and symptoms is essential in the fight against diseases. Many attempts have been made to introduce the…
Deep Incomplete Multi-View Multiple Clusterings
Shaowei Wei, Jun Wang, Guoxian Yu +2
Multi-view clustering aims at exploiting information from multiple heterogeneous views to promote clustering. Most previous works search for only one optimal clustering based on th…
SAIL: Self-Augmented Graph Contrastive Learning
Lu Yu, Shichao Pei, Lizhong Ding +4
This paper studies learning node representations with graph neural networks (GNNs) for unsupervised scenario. Specifically, we derive a theoretical analysis and provide an empirica…
PAGE: A Simple and Optimal Probabilistic Gradient Estimator for Nonconvex Optimization
Zhize Li, Hongyan Bao, Xiangliang Zhang +1
In this paper, we propose a novel stochastic gradient estimator -- ProbAbilistic Gradient Estimator (PAGE) -- for nonconvex optimization. PAGE is easy to implement as it is designe…
Attention-Aware Answers of the Crowd
Jingzheng Tu, Guoxian Yu, Jun Wang +2
Crowdsourcing is a relatively economic and efficient solution to collect annotations from the crowd through online platforms. Answers collected from workers with different expertis…
Multi-View Multiple Clusterings using Deep Matrix Factorization
Shaowei Wei, Jun Wang, Guoxian Yu +2
Multi-view clustering aims at integrating complementary information from multiple heterogeneous views to improve clustering results. Existing multi-view clustering solutions can on…
Collaborative Graph Walk for Semi-supervised Multi-Label Node Classification
Uchenna Akujuobi, Han Yufei, Qiannan Zhang +1
In this work, we study semi-supervised multi-label node classification problem in attributed graphs. Classic solutions to multi-label node classification follow two steps, first le…
Active Multi-Label Crowd Consensus
Jinzheng Tu, Guoxian Yu, Carlotta Domeniconi +2
Crowdsourcing is an economic and efficient strategy aimed at collecting annotations of data through an online platform. Crowd workers with different expertise are paid for their se…
Prototypical Networks for Multi-Label Learning
Zhuo Yang, Yufei Han, Guoxian Yu +2
We propose to formulate multi-label learning as a estimation of class distribution in a non-linear embedding space, where for each label, its positive data embeddings and negative…
Recurrent Attention Walk for Semi-supervised Classification
Uchenna Akujuobi, Qiannan Zhang, Han Yufei +1
In this paper, we study the graph-based semi-supervised learning for classifying nodes in attributed networks, where the nodes and edges possess content information. Recent approac…
PINE: Universal Deep Embedding for Graph Nodes via Partial Permutation Invariant Set Functions
Shupeng Gui, Xiangliang Zhang, Pan Zhong +5
Graph node embedding aims at learning a vector representation for all nodes given a graph. It is a central problem in many machine learning tasks (e.g., node classification, recomm…
Cross-modal Zero-shot Hashing
Xuanwu Liu, Zhao Li, Jun Wang +3
Hashing has been widely studied for big data retrieval due to its low storage cost and fast query speed. Zero-shot hashing (ZSH) aims to learn a hashing model that is trained using…