69 citations · 336 across the 32 of their papers we have counts for
14 papers · 1 filter
Finding Short Signals in Long Irregular Time Series with Continuous-Time Attention Policy Networks
Thomas Hartvigsen, Jidapa Thadajarassiri, Xiangnan Kong +1
Irregularly-sampled time series (ITS) are native to high-impact domains like healthcare, where measurements are collected over time at uneven intervals. However, for many classific…
Stop&Hop: Early Classification of Irregular Time Series
Thomas Hartvigsen, Walter Gerych, Jidapa Thadajarassiri +2
Early classification algorithms help users react faster to their machine learning model's predictions. Early warning systems in hospitals, for example, let clinicians improve their…
One-Shot Learning on Attributed Sequences
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
One-shot learning has become an important research topic in the last decade with many real-world applications. The goal of one-shot learning is to classify unlabeled instances when…
Self-learn to Explain Siamese Networks Robustly
Chao Chen, Yifan Shen, Guixiang Ma +4
Learning to compare two objects are essential in applications, such as digital forensics, face recognition, and brain network analysis, especially when labeled data is scarce and i…
Gaussian Mixture Graphical Lasso with Application to Edge Detection in Brain Networks
Hang Yin, Xinyue Liu, Xiangnan Kong
Sparse inverse covariance estimation (i.e., edge de-tection) is an important research problem in recent years, wherethe goal is to discover the direct connections between a set ofn…
MLAS: Metric Learning on Attributed Sequences
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
Distance metric learning has attracted much attention in recent years, where the goal is to learn a distance metric based on user feedback. Conventional approaches to metric learni…
Attributed Sequence Embedding
Zhongfang Zhuang, Xiangnan Kong, Elke Rundensteiner +2
Mining tasks over sequential data, such as clickstreams and gene sequences, require a careful design of embeddings usable by learning algorithms. Recent research in feature learnin…
Deep Graph Attention Model
John Boaz Lee, Ryan Rossi, Xiangnan Kong
Graph classification is a problem with practical applications in many different domains. Most of the existing methods take the entire graph into account when calculating graph feat…
Mining Brain Networks using Multiple Side Views for Neurological Disorder Identification
Bokai Cao, Xiangnan Kong, Jingyuan Zhang +2
Mining discriminative subgraph patterns from graph data has attracted great interest in recent years. It has a wide variety of applications in disease diagnosis, neuroimaging, etc.…
A review of heterogeneous data mining for brain disorders
Bokai Cao, Xiangnan Kong, Philip S. Yu
With rapid advances in neuroimaging techniques, the research on brain disorder identification has become an emerging area in the data mining community. Brain disorder data poses ma…
DuSK: A Dual Structure-preserving Kernel for Supervised Tensor Learning with Applications to Neuroimages
Lifang He, Xiangnan Kong, Philip S. Yu +3
With advances in data collection technologies, tensor data is assuming increasing prominence in many applications and the problem of supervised tensor learning has emerged as a top…
Large-Scale Multi-Label Learning with Incomplete Label Assignments
Xiangnan Kong, Zhaoming Wu, Li-Jia Li +4
Multi-label learning deals with the classification problems where each instance can be assigned with multiple labels simultaneously. Conventional multi-label learning approaches ma…
Meta Path-Based Collective Classification in Heterogeneous Information Networks
Xiangnan Kong, Bokai Cao, Philip S. Yu +2
Collective classification has been intensively studied due to its impact in many important applications, such as web mining, bioinformatics and citation analysis. Collective classi…
Discriminative Feature Selection for Uncertain Graph Classification
Xiangnan Kong, Philip S. Yu, Xue Wang +1
Mining discriminative features for graph data has attracted much attention in recent years due to its important role in constructing graph classifiers, generating graph indices, et…