Tongliang Liu

is this you? claim profile

∙ 0 followers
University of Technology Sydney ∙ The University of Sydney

Lecturer in machine learning at the School of Information Technologies, The University of Sydney. He received the BEng degree in electronic engineering and information science from the University of Science and Technology of China, and the PhD degree from the University of Technology Sydney. From October 2015 to March 2016, he was a visiting PhD student with Barcelona Graduate School of Economics (Barcelona GSE) and the Department of Economics at Pompeu Fabra University, Spain.

Featured Co-authors

Dacheng Tao
210 publications
Wei Liu
202 publications
Masashi Sugiyama
142 publications
Jie Yang
79 publications
Larry S. Davis
78 publications
Wanli Ouyang
76 publications
Chang Xu
63 publications
Jian Yang
62 publications
Kun Zhang
57 publications
Wen Gao
54 publications
Gang Niu
46 publications

A Regularization Approach for Instance-Based Superset Label Learning

Different from the traditional supervised learning in which each trainin...
04/05/2019 ∙ by Chen gong, et al. ∙ 24 ∙ share

read it
A Shape Transformation-based Dataset Augmentation Framework for Pedestrian Detection

Deep learning-based computer vision is usually data-hungry. Many researc...
12/15/2019 ∙ by Zhe Chen, et al. ∙ 23 ∙ share

read it
Continuous Dropout

Dropout has been proven to be an effective algorithm for training robust...
11/28/2019 ∙ by Xu Shen, et al. ∙ 16 ∙ share

read it
Transferring Knowledge Fragments for Learning Distance Metric from A Heterogeneous Domain

The goal of transfer learning is to improve the performance of target le...
04/08/2019 ∙ by Yong Luo, et al. ∙ 14 ∙ share

read it
Multi-Class Classification from Noisy-Similarity-Labeled Data

A similarity label indicates whether two instances belong to the same cl...
02/16/2020 ∙ by Songhua Wu, et al. ∙ 9 ∙ share

read it
Orthogonal Deep Neural Networks

In this paper, we introduce the algorithms of Orthogonal Deep Neural Net...
05/15/2019 ∙ by Kui Jia, et al. ∙ 6 ∙ share

read it
Confidence Scores Make Instance-dependent Label-noise Learning Possible

Learning with noisy labels has drawn a lot of attention. In this area, m...
01/11/2020 ∙ by Antonin Berthon, et al. ∙ 4 ∙ share

read it
Towards Mixture Proportion Estimation without Irreducibility

Mixture proportion estimation (MPE) is a fundamental problem of practica...
02/10/2020 ∙ by Yu Yao, et al. ∙ 1 ∙ share

read it
Learning with Biased Complementary Labels

In this paper we study the classification problem in which we have acces...
11/27/2017 ∙ by Xiyu Yu, et al. ∙ 0 ∙ share

read it
Learning with Bounded Instance- and Label-dependent Label Noise

Instance- and label-dependent label noise (ILN) is widely existed in rea...
09/12/2017 ∙ by Jiacheng Cheng, et al. ∙ 0 ∙ share

read it
Transfer Learning with Label Noise

Transfer learning aims to improve learning in the target domain with lim...
07/31/2017 ∙ by Xiyu Yu, et al. ∙ 0 ∙ share

read it
Algorithmic stability and hypothesis complexity

We introduce a notion of algorithmic stability of learning algorithms---...
02/28/2017 ∙ by Tongliang Liu, et al. ∙ 0 ∙ share

read it
Local Blur Mapping: Exploiting High-Level Semantics by Deep Neural Networks

The human visual system excels at detecting local blur of visual images,...
12/05/2016 ∙ by Kede Ma, et al. ∙ 0 ∙ share

read it
Dimensionality-Dependent Generalization Bounds for k-Dimensional Coding Schemes

The k-dimensional coding schemes refer to a collection of methods that a...
01/03/2016 ∙ by Tongliang Liu, et al. ∙ 0 ∙ share

read it
Classification with Noisy Labels by Importance Reweighting

In this paper, we study a classification problem in which sample labels ...
11/27/2014 ∙ by Tongliang Liu, et al. ∙ 0 ∙ share

read it
Local Rademacher Complexity for Multi-label Learning

We analyze the local Rademacher complexity of empirical risk minimizatio...
10/26/2014 ∙ by Chang Xu, et al. ∙ 0 ∙ share

read it
Elastic Net Hypergraph Learning for Image Clustering and Semi-supervised Classification

Graph model is emerging as a very effective tool for learning the comple...
03/03/2016 ∙ by Qingshan Liu, et al. ∙ 0 ∙ share

read it
On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier

We study the rates of convergence from empirical surrogate risk minimize...
02/11/2018 ∙ by Jingwei Zhang, et al. ∙ 0 ∙ share

read it
An Information-Theoretic View for Deep Learning

Deep learning has transformed the computer vision, natural language proc...
04/24/2018 ∙ by Jingwei Zhang, et al. ∙ 0 ∙ share

read it
Domain Generalization via Conditional Invariant Representation

Domain generalization aims to apply knowledge gained from multiple label...
07/23/2018 ∙ by Ya Li, et al. ∙ 0 ∙ share

read it
Instance-Dependent PU Learning by Bayesian Optimal Relabeling

When learning from positive and unlabelled data, it is a strong assumpti...
08/07/2018 ∙ by Fengxiang He, et al. ∙ 0 ∙ share

read it
Implementable Quantum Classifier for Nonlinear Data

In this Letter, we propose a quantum machine learning scheme for the cla...
09/17/2018 ∙ by Yuxuan Du, et al. ∙ 0 ∙ share

read it
The Expressive Power of Parameterized Quantum Circuits

Parameterized quantum circuits (PQCs) have been broadly used as a hybrid...
10/29/2018 ∙ by Yuxuan Du, et al. ∙ 0 ∙ share

read it
An Optimal Transport View on Generalization

We derive upper bounds on the generalization error of learning algorithm...
11/08/2018 ∙ by Jingwei Zhang, et al. ∙ 0 ∙ share

read it
Robust Angular Local Descriptor Learning

In recent years, the learned local descriptors have outperformed handcra...
01/21/2019 ∙ by Yanwu Xu, et al. ∙ 0 ∙ share

read it
Generative-Discriminative Complementary Learning

Majority of state-of-the-art deep learning methods for vision applicatio...
04/02/2019 ∙ by Yanwu Xu, et al. ∙ 0 ∙ share

read it
Why ResNet Works? Residuals Generalize

Residual connections significantly boost the performance of deep neural ...
04/02/2019 ∙ by Fengxiang He, et al. ∙ 0 ∙ share

read it
Decomposition-Based Transfer Distance Metric Learning for Image Classification

Distance metric learning (DML) is a critical factor for image analysis a...
04/08/2019 ∙ by Yong Luo, et al. ∙ 0 ∙ share

read it
Adaptive Morphological Reconstruction for Seeded Image Segmentation

Morphological reconstruction (MR) is often employed by seeded image segm...
04/08/2019 ∙ by Tao Lei, et al. ∙ 0 ∙ share

read it
Fast Supervised Discrete Hashing

Learning-based hashing algorithms are "hot topics" because they can grea...
04/07/2019 ∙ by Jie Gui, et al. ∙ 0 ∙ share

read it
Supervised Discrete Hashing with Relaxation

Data-dependent hashing has recently attracted attention due to being abl...
04/07/2019 ∙ by Jie Gui, et al. ∙ 0 ∙ share

read it
Multi-View Matrix Completion for Multi-Label Image Classification

There is growing interest in multi-label image classification due to its...
04/08/2019 ∙ by Yong Luo, et al. ∙ 0 ∙ share

read it
On Better Exploring and Exploiting Task Relationships in Multi-Task Learning: Joint Model and Feature Learning

Multitask learning (MTL) aims to learn multiple tasks simultaneously thr...
04/03/2019 ∙ by Ya Li, et al. ∙ 0 ∙ share

read it
DistillHash: Unsupervised Deep Hashing by Distilling Data Pairs

Due to the high storage and search efficiency, hashing has become preval...
05/09/2019 ∙ by Erkun Yang, et al. ∙ 0 ∙ share

read it
Are Anchor Points Really Indispensable in Label-Noise Learning?

In label-noise learning, noise transition matrix, denoting the probabili...
06/01/2019 ∙ by Xiaobo Xia, et al. ∙ 0 ∙ share

read it
Truncated Cauchy Non-negative Matrix Factorization

Non-negative matrix factorization (NMF) minimizes the Euclidean distance...
06/02/2019 ∙ by Naiyang Guan, et al. ∙ 0 ∙ share

read it
Towards Digital Retina in Smart Cities: A Model Generation, Utilization and Communication Paradigm

The digital retina in smart cities is to select what the City Eye tells ...
07/31/2019 ∙ by Yihang Lou, et al. ∙ 0 ∙ share

read it
dipIQ: Blind Image Quality Assessment by Learning-to-Rank Discriminable Image Pairs

Objective assessment of image quality is fundamentally important in many...
04/13/2019 ∙ by Kede Ma, et al. ∙ 0 ∙ share

read it
Where is the Bottleneck of Adversarial Learning with Unlabeled Data?

Deep neural networks (DNNs) are incredibly brittle due to adversarial ex...
11/20/2019 ∙ by Jingfeng Zhang, et al. ∙ 0 ∙ share

read it
A Quantum-inspired Algorithm for General Minimum Conical Hull Problems

A wide range of fundamental machine learning tasks that are addressed by...
07/16/2019 ∙ by Yuxuan Du, et al. ∙ 0 ∙ share

read it
Quantum noise protects quantum classifiers against adversaries

Noise in quantum information processing is often viewed as a disruptive ...
03/20/2020 ∙ by Yuxuan Du, et al. ∙ 0 ∙ share

read it
Repulsive Mixture Models of Exponential Family PCA for Clustering

The mixture extension of exponential family principal component analysis...
04/07/2020 ∙ by Maoying Qiao, et al. ∙ 0 ∙ share

read it
Parts-dependent Label Noise: Towards Instance-dependent Label Noise

Learning with the instance-dependent label noise is challenging, because...
06/14/2020 ∙ by Xiaobo Xia, et al. ∙ 0 ∙ share

read it
Class2Simi: A New Perspective on Learning with Label Noise

Label noise is ubiquitous in the era of big data. Deep learning algorith...
06/14/2020 ∙ by Songhua Wu, et al. ∙ 0 ∙ share

read it
Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning

The transition matrix, denoting the transition relationship from clean l...
06/14/2020 ∙ by Yu Yao, et al. ∙ 0 ∙ share

read it