119 citations · 294 across the 20 of their papers we have counts for
20 papers
GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification
Daoming Zong, Shiliang Sun
Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent…
Manifold Partition Discriminant Analysis
Yang Zhou, Shiliang Sun
We propose a novel algorithm for supervised dimensionality reduction named Manifold Partition Discriminant Analysis (MPDA). It aims to find a linear embedding space where the withi…
Adversarial Attacks for Multi-view Deep Models
Xuli Sun, Shiliang Sun
Recent work has highlighted the vulnerability of many deep machine learning models to adversarial examples. It attracts increasing attention to adversarial attacks, which can be us…
Adversarial Attack with Pattern Replacement
Ziang Dong, Liang Mao, Shiliang Sun
We propose a generative model for adversarial attack. The model generates subtle but predictive patterns from the input. To perform an attack, it replaces the patterns of the input…
A Variant of Gaussian Process Dynamical Systems
Jing Zhao, Jingjing Fei, Shiliang Sun
In order to better model high-dimensional sequential data, we propose a collaborative multi-output Gaussian process dynamical system (CGPDS), which is a novel variant of GPDSs. The…
A Survey of Optimization Methods from a Machine Learning Perspective
Shiliang Sun, Zehui Cao, Han Zhu +1
Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields. Optimization, as an important part of machine learning, ha…
Variational Langevin Hamiltonian Monte Carlo for Distant Multi-modal Sampling
Minghao Gu, Shiliang Sun
The Hamiltonian Monte Carlo (HMC) sampling algorithm exploits Hamiltonian dynamics to construct efficient Markov Chain Monte Carlo (MCMC), which has become increasingly popular in…
Online Anomaly Detection with Sparse Gaussian Processes
Jingjing Fei, Shiliang Sun
Online anomaly detection of time-series data is an important and challenging task in machine learning. Gaussian processes (GPs) are powerful and flexible models for modeling time-s…
PAC-Bayes bounds for stable algorithms with instance-dependent priors
Omar Rivasplata, Emilio Parrado-Hernandez, John Shawe-Taylor +2
PAC-Bayes bounds have been proposed to get risk estimates based on a training sample. In this paper the PAC-Bayes approach is combined with stability of the hypothesis learned by a…
Network-Scale Traffic Modeling and Forecasting with Graphical Lasso and Neural Networks
Shiliang Sun, Rongqing Huang, Ya Gao
Traffic flow forecasting, especially the short-term case, is an important topic in intelligent transportation systems (ITS). This paper does a lot of research on network-scale mode…
Kernel Regression with Sparse Metric Learning
Rongqing Huang, Shiliang Sun
Kernel regression is a popular non-parametric fitting technique. It aims at learning a function which estimates the targets for test inputs as precise as possible. Generally, the f…
Weighted Data Normalization Based on Eigenvalues for Artificial Neural Network Classification
Qingjiu Zhang, Shiliang Sun
Artificial neural network (ANN) is a very useful tool in solving learning problems. Boosting the performances of ANN can be mainly concluded from two aspects: optimizing the archit…
Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor
Shiliang Sun, Changshui Zhang, Yi Zhang
A novel predictor for traffic flow forecasting, namely spatio-temporal Bayesian network predictor, is proposed. Unlike existing methods, our approach incorporates all the spatial a…
Neural Network Multitask Learning for Traffic Flow Forecasting
Feng Jin, Shiliang Sun
Traditional neural network approaches for traffic flow forecasting are usually single task learning (STL) models, which do not take advantage of the information provided by related…
A Survey on Multi-View Clustering
Guoqing Chao, Shiliang Sun, Jinbo Bi
With advances in information acquisition technologies, multi-view data become ubiquitous. Multi-view learning has thus become more and more popular in machine learning and data min…
Location Dependent Dirichlet Processes
Shiliang Sun, John Paisley, Qiuyang Liu
Dirichlet processes (DP) are widely applied in Bayesian nonparametric modeling. However, in their basic form they do not directly integrate dependency information among data arisin…
Multi-view Regularized Gaussian Processes
Qiuyang Liu, Shiliang Sun
Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view l…
PAC-Bayes Analysis of Multi-view Learning
Shiliang Sun, John Shawe-Taylor, Liang Mao
This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers. These bounds adopt data dependent Gaussian priors which emphasize cl…
Infinite Mixtures of Multivariate Gaussian Processes
Shiliang Sun
This paper presents a new model called infinite mixtures of multivariate Gaussian processes, which can be used to learn vector-valued functions and applied to multitask learning. A…
Multi-view Laplacian Support Vector Machines
Shiliang Sun
We propose a new approach, multi-view Laplacian support vector machines (SVMs), for semi-supervised learning under the multi-view scenario. It integrates manifold regularization an…