Search Results for author: Shiliang Sun

Found 29 papers, 1 papers with code

Semismooth Newton Algorithm for Efficient Projections onto $\ell_{1, \infty}$-norm Ball

1 code implementation ICML 2020 Dejun Chu, Chang-Shui Zhang, Shiliang Sun, Qing Tao

Structured sparsity-inducing $\ell_{1, \infty}$-norm, as a generalization of the classical $\ell_1$-norm, plays an important role in jointly sparse models which select or remove simultaneously all the variables forming a group.

GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification

no code implementations10 Dec 2020 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.

General Classification Graph Clustering +6

Manifold Partition Discriminant Analysis

no code implementations23 Nov 2020 Yang Zhou, Shiliang Sun

We propose a novel algorithm for supervised dimensionality reduction named Manifold Partition Discriminant Analysis (MPDA).

Supervised dimensionality reduction

Adversarial Attacks for Multi-view Deep Models

no code implementations19 Jun 2020 Xuli Sun, Shiliang Sun

With the mild assumption that the single-view model on which the target multi-view model is based is known, we first propose the TSA strategy.

Adversarial Attack

A Survey of Optimization Methods from a Machine Learning Perspective

no code implementations17 Jun 2019 Shiliang Sun, Zehui Cao, Han Zhu, Jing Zhao

Machine learning develops rapidly, which has made many theoretical breakthroughs and is widely applied in various fields.

A Variant of Gaussian Process Dynamical Systems

no code implementations9 Jun 2019 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.

Variational Inference

Variational Langevin Hamiltonian Monte Carlo for Distant Multi-modal Sampling

no code implementations1 Jun 2019 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 machine learning and statistics.

Online Anomaly Detection with Sparse Gaussian Processes

no code implementations14 May 2019 Jingjing Fei, Shiliang Sun

Moreover, the SGP-Q makes use of few abnormal data in the training data by its strategy of updating training data, resulting in more accurate sparse Gaussian process regression models and better anomaly detection results.

Anomaly Detection Gaussian Processes +1

Kernel Regression with Sparse Metric Learning

no code implementations25 Dec 2017 Rongqing Huang, Shiliang Sun

Generally, the function value for a test input is estimated by a weighted average of the surrounding training examples.

Dimensionality Reduction Metric Learning

Neural Network Multitask Learning for Traffic Flow Forecasting

no code implementations24 Dec 2017 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 tasks.

Traffic Flow Forecasting Using a Spatio-Temporal Bayesian Network Predictor

no code implementations24 Dec 2017 Shiliang Sun, Chang-Shui Zhang, Yi Zhang

A novel predictor for traffic flow forecasting, namely spatio-temporal Bayesian network predictor, is proposed.

A Survey on Multi-View Clustering

no code implementations18 Dec 2017 Guoqing Chao, Shiliang Sun, Jinbo Bi

With advances in information acquisition technologies, multi-view data become ubiquitous.


Multi-view Regularized Gaussian Processes

no code implementations17 Jan 2017 Qiuyang Liu, Shiliang Sun

Moreover, we give a general point selection scheme for multi-view learning and improve the proposed model by this criterion.

Gaussian Processes MULTI-VIEW LEARNING

PAC-Bayes Analysis of Multi-view Learning

no code implementations21 Jun 2014 Shiliang Sun, John Shawe-Taylor, Liang Mao

This paper presents eight PAC-Bayes bounds to analyze the generalization performance of multi-view classifiers.


Infinite Mixtures of Multivariate Gaussian Processes

no code implementations26 Jul 2013 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.

Gaussian Processes

Multi-view Laplacian Support Vector Machines

no code implementations26 Jul 2013 Shiliang Sun

It integrates manifold regularization and multi-view regularization into the usual formulation of SVMs and is a natural extension of SVMs from supervised learning to multi-view semi-supervised learning.

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