41 citations · 118 across the 21 of their papers we have counts for
9 papers · 1 filter
Leveraging Multi-level Dependency of Relational Sequences for Social Spammer Detection
Jun Yin, Qian Li, Shaowu Liu +2
Much recent research has shed light on the development of the relation-dependent but content-independent framework for social spammer detection. This is largely because the relatio…
Causality Learning: A New Perspective for Interpretable Machine Learning
Guandong Xu, Tri Dung Duong, Qian Li +2
Recent years have witnessed the rapid growth of machine learning in a wide range of fields such as image recognition, text classification, credit scoring prediction, recommendation…
Generative Temporal Link Prediction via Self-tokenized Sequence Modeling
Yue Wang, Chenwei Zhang, Shen Wang +4
We formalize networks with evolving structures as temporal networks and propose a generative link prediction model, Generative Link Sequence Modeling (GLSM), to predict future link…
Target-Independent Active Learning via Distribution-Splitting
Xiaofeng Cao, Ivor W. Tsang, Xiaofeng Xu +1
To reduce the label complexity in Agnostic Active Learning (A^2 algorithm), volume-splitting splits the hypothesis edges to reduce the Vapnik-Chervonenkis (VC) dimension in version…
A Structured Perspective of Volumes on Active Learning
Xiaofeng Cao, Ivor W. Tsang, Guandong Xu
Active Learning (AL) is a learning task that requires learners interactively query the labels of the sampled unlabeled instances to minimize the training outputs with human supervi…
A Divide-and-Conquer Approach to Geometric Sampling for Active Learning
Xiaofeng Cao
Active learning (AL) repeatedly trains the classifier with the minimum labeling budget to improve the current classification model. The training process is usually supervised by an…
CSAL: Self-adaptive Labeling based Clustering Integrating Supervised Learning on Unlabeled Data
Fangfang Li, Guandong Xu, Longbing Cao
Supervised classification approaches can predict labels for unknown data because of the supervised training process. The success of classification is heavily dependent on the label…
Heterogeneous Metric Learning with Content-based Regularization for Software Artifact Retrieval
Liang Wu, Hui Xiong, Liang Du +6
The problem of software artifact retrieval has the goal to effectively locate software artifacts, such as a piece of source code, in a large code repository. This problem has been…
Coupled Item-based Matrix Factorization
Fangfang Li, Guandong Xu, Longbing Cao
The essence of the challenges cold start and sparsity in Recommender Systems (RS) is that the extant techniques, such as Collaborative Filtering (CF) and Matrix Factorization (MF),…