Tongliang Liu

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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
221 publications
Wei Liu
211 publications
Masashi Sugiyama
145 publications
Jie Yang
85 publications
Wanli Ouyang
82 publications
Larry S. Davis
80 publications
Jie Li
80 publications
Jian Yang
71 publications
Chang Xu
68 publications
Kun Zhang
59 publications
Wen Gao
56 publications

Quantum differentially private sparse regression learning

Differentially private (DP) learning, which aims to accurately extract p...
07/23/2020 ∙ by Yuxuan Du, et al. ∙ 0 ∙ share

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Weakly Supervised Temporal Action Localization with Segment-Level Labels

Temporal action localization presents a trade-off between test performan...
07/03/2020 ∙ by Xinpeng Ding, et al. ∙ 0 ∙ share

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Continuous Dropout

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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Why ResNet Works? Residuals Generalize

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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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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

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