I. Tsang

发表

Kernel relevant component analysis for distance metric learning

I. Tsang, Pak-Ming Cheung, J. Kwok, 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

Learning Category-Specific Dictionary and Shared Dictionary for Fine-Grained Image Categorization

Yi Ma, I. Tsang, Shenghua Gao, 2014, IEEE Transactions on Image Processing.

om Low-Rank Approximation and Error Analysis

I. Tsang, J. Kwok, Kai Zhang, 2008 .

Domain Adaptation via Transfer Component Analysis

Ivor W. Tsang, Qiang Yang, James T. Kwok, 2009, IEEE Transactions on Neural Networks.

Authors' Reply to the "Comments on the Core Vector Machines: Fast SVM Training on Very Large Data Sets"

I. Tsang, J. Kwok, 2007 .

Progressive Stochastic Learning for Noisy Labels

Ivor W. Tsang, Ling Chen, Bo Han, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Distance metric learning with kernels

Ivor W. Tsang, James T. Kwok, I. Tsang, 2003 .

Core Vector Machines: Fast SVM Training on Very Large Data Sets

Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung, 2005, J. Mach. Learn. Res..

Domain Transfer SVM for video concept detection

Ivor W. Tsang, Lixin Duan, Stephen J. Maybank, 2009, CVPR 2009.

Improved Nyström low-rank approximation and error analysis

Ivor W. Tsang, James T. Kwok, Kai Zhang, 2008, ICML '08.

Learning with Idealized Kernels

Ivor W. Tsang, James T. Kwok, I. Tsang, 2003, ICML.

Flexible Manifold Embedding: A Framework for Semi-Supervised and Unsupervised Dimension Reduction

Ivor W. Tsang, Dong Xu, Feiping Nie, 2010, IEEE Transactions on Image Processing.

Maximum Margin Clustering Made Practical

Ivor W. Tsang, James T. Kwok, Kai Zhang, 2007, IEEE Transactions on Neural Networks.

Efficient Optimization of Performance Measures by Classifier Adaptation

Ivor W. Tsang, Zhi-Hua Zhou, Nan Li, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Multi-layer group sparse coding — For concurrent image classification and annotation

Liang-Tien Chia, Ivor W. Tsang, Shenghua Gao, 2011, CVPR 2011.

Riemannian Pursuit for Big Matrix Recovery

Ivor W. Tsang, Mingkui Tan, Sinno Jialin Pan, 2014, ICML.

Core Vector Regression for very large regression problems

Ivor W. Tsang, James T. Kwok, Kimo T. Lai, 2005, ICML.

Tighter and Convex Maximum Margin Clustering

Ivor W. Tsang, Zhi-Hua Zhou, James T. Kwok, 2009, AISTATS.

Text-based image retrieval using progressive multi-instance learning

Ivor W. Tsang, Dong Xu, Wen Li, 2011, 2011 International Conference on Computer Vision.

Domain Transfer Multiple Kernel Learning

Ivor W. Tsang, Dong Xu, Lixin Duan, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation

Ivor W. Tsang, Dong Xu, Wen Li, 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Unseen Land Cover Classification from High-Resolution Orthophotos Using Integration of Zero-Shot Learning and Convolutional Neural Networks

Biswajeet Pradhan, Maher Ibrahim Sameen, Husam Abdulrasool H. Al-Najjar, 2020, Remote. Sens..

Discovering Low-Rank Shared Concept Space for Adapting Text Mining Models

Ivor W. Tsang, Bo Chen, Wai Lam, 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Intrinsic Reward Driven Imitation Learning via Generative Model pdf

Xingrui Yu, Yueming Lyu, Ivor W. Tsang, 2020, ICML.

Principal Graph and Structure Learning Based on Reversed Graph Embedding

Li Wang, Ivor W. Tsang, Qi Mao, 2017, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Supplementary Materials for "Riemannian Pursuit for Big Matrix Recovery"

Sinno Jialin Pan, I. Tsang, Bart Vandereycken, 2014 .

Local features are not lonely – Laplacian sparse coding for image classification

Liang-Tien Chia, Ivor W. Tsang, Shenghua Gao, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Title Concurrent Single-Label Image Classification andAnnotation via Efficient Multi-Layer Group Sparse Coding

I. Tsang, Zhixiang Ren, 2019 .

Clustering-based methodology with minimal user supervision for displaying cell-phenotype signatures in image-based screening

I. Tsang, Y. Ong, F. Bard, 2010, 2010 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW).

Co-teaching + : Towards Training of Robust Deep Networks with Noisy Labels

Masashi Sugiyama, I. Tsang, Bo Han, 2019 .

An End-to-End Sparse Coding

Sinno Jialin Pan, Joey Tianyi Zhou, I. Tsang, 2017 .

Title Region-Based Saliency Detection and Its Application inObject Recognition

I. Tsang, Shenghua Gao, L. Chia, 2019 .

Efficient Multi-template Learning for Structured Prediction the Major Advantage of Structured Prediction Models Such as Conditional Ran- Dom Fields (crfs) [18] and Structural Support Vector Machines (structural Svms)

I. Tsang, 2013 .

The Emerging Trends of Multi-Label Learning pdf

Ivor W. Tsang, Xiaobo Shen, Weiwei Liu, 2020, IEEE transactions on pattern analysis and machine intelligence.

Mental Fatigue Monitoring using Brain Dynamics Preferences

Avinash Kumar Singh, I. Tsang, Chin-Teng Lin, 2018 .

Online Multi-Agent Forecasting with Interpretable Collaborative Graph Neural Network pdf

Ivor W. Tsang, Siheng Chen, Maosen Li, 2021, IEEE transactions on neural networks and learning systems.

Position estimation tor wireless sensor networks

Ivor W. Tsang, K. F. Simon Wong, Victor Cheung, 2005 .

Learning 3-opt heuristics for traveling salesman problem via deep reinforcement learning

I. Tsang, Jingyan Sui, Shizhe Ding, 2021 .

Efficient federated multi-view learning

Zenglin Xu, I. Tsang, Shudong Huang, 2022, Pattern Recognit..

2014 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2014, Columbus, OH, USA, June 23-28, 2014

Stefanos Zafeiriou, Arif Mahmood, Ivor W. Tsang, 2014, CVPR.

Recursive Copy and Paste GAN: Face Hallucination From Shaded Thumbnails

I. Tsang, Yang Zhang, Changhui Hu, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

When an Active Learner Meets a Black-box Teacher

I. Tsang, Xiaofeng Cao, Yaming Guo, 2022, ArXiv.

Data-Efficient Learning via Minimizing Hyperspherical Energy

I. Tsang, Weiyang Liu, Xiaofeng Cao, 2022, ArXiv.

Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation.

I. Tsang, Wen Li, Lixin Duan, 2014, IEEE transactions on pattern analysis and machine intelligence.

Simpler core vector machines with enclosing balls

I. Tsang, J. Kwok, A. Kocsor, 2007, ICML '07.

MULTI-LABEL METRIC LEARNING WITH BIDIRECTIONAL REPRESENTATION DEEP NEURAL NETWORKS

Ivor W. Tsang, Xin Yao, Tao Zheng, 2019 .

Cold-Start Active Sampling Via γ-Tube

I. Tsang, Xiaofeng Cao, Jianliang Xu, 2021, IEEE Transactions on Cybernetics.

Reasonable Effectiveness of Random Weighting: A Litmus Test for Multi-Task Learning

I. Tsang, Yu Zhang, Baijiong Lin, 2021, Trans. Mach. Learn. Res..

A Survey of Learning on Small Data

I. Tsang, Xiaofeng Cao, Yaming Guo, 2022, ArXiv.

Data-Efficient Learning via Minimizing Hyperspherical Energy

I. Tsang, Weiyang Liu, Xiaofeng Cao, 2022, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Contrastive Attraction and Contrastive Repulsion for Representation Learning

I. Tsang, Mingyuan Zhou, Jingren Zhou, 2021, 2105.03746.

Differential-Critic GAN: Generating What You Want by a Cue of Preferences

X. Yao, I. Tsang, Yuangang Pan, 2021, IEEE transactions on neural networks and learning systems.

Finding the pre-images in kernel principal component analysis

Ivor W. Tsang, James T. Kwok, I. Tsang, 2002 .

Domain Adaptation via Transfer Component Analysis — Source link

Sinno Jialin Pan, I. Tsang, J. Kwok, 2022 .

Multi-View Clustering on Topological Manifold

Zenglin Xu, I. Tsang, Shudong Huang, 2022, AAAI.

Institutional Knowledge at Singapore Management University Institutional Knowledge at Singapore Management University Two-Layer Multiple Kernel Learning Two-Layer Multiple Kernel Learning

I. Tsang, S. Hoi, Jinfeng Zhuang, 2020 .

Deep Safe Multi-Task Learning

I. Tsang, Yu Zhang, Feiyang Ye, 2021, 2111.10601.

Collaborative Generated Hashing for Market Analysis and Fast Cold-start Recommendation

Guowu Yang, Ivor W. Tsang, Yan Zhang, 2019 .

Learning Smooth Representation for Multi-view Subspace Clustering

I. Tsang, Yazhou Ren, Shudong Huang, 2022, ACM Multimedia.

Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling

I. Tsang, Bowen Xing, 2022, ArXiv.

Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs

I. Tsang, Bowen Xing, 2022, 2210.10375.

Reasonable Eﬀectiveness of Random Weighting: A Litmus Test for Multi-Task Learning

I. Tsang, Baijiong Lin, Feiyang Ye, 2022 .

Learning With Label Proportions by Incorporating Unmarked Data

I. Tsang, Jing Chai, 2021, IEEE Transactions on Neural Networks and Learning Systems.

CIKM '15 Proceedings of the 24th ACM International on Conference on Information and Knowledge Management

Ivor W. Tsang, Ling Chen, Haishuai Wang, 2015 .

Deep Discrete Prototype Multilabel Learning

Weiwei Liu, Yong Luo, Ivor W. Tsang, 2018, IJCAI.

Group is better than individual: Exploiting Label Topologies and Label Relations for Joint Multiple Intent Detection and Slot Filling

I. Tsang, Bowen Xing, 2022, EMNLP.

Co-guiding Net: Achieving Mutual Guidances between Multiple Intent Detection and Slot Filling via Heterogeneous Semantics-Label Graphs

I. Tsang, Bowen Xing, 2022, EMNLP.

One-shot Machine Teaching: Cost Very Few Examples to Converge Faster

I. Tsang, Xiaofeng Cao, Chen Zhang, 2022, ArXiv.

Coarse-to-Fine Contrastive Learning on Graphs

I. Tsang, L. Liao, Yuangang Pan, 2022, IEEE transactions on neural networks and learning systems.

Memes as building blocks: a case study on evolutionary optimization + transfer learning for routing problems

I. Tsang, Y. Ong, Liang Feng, 2015, Memetic Computing.

Sparse Representation With Kernels

Liang-Tien Chia, Ivor W. Tsang, Shenghua Gao, 2013, IEEE Transactions on Image Processing.

Laplacian Sparse Coding, Hypergraph Laplacian Sparse Coding, and Applications

Liang-Tien Chia, Shenghua Gao, Ivor Wai-Hung Tsang, 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Neural Optimization Kernel: Towards Robust Deep Learning pdf

Yueming Lyu, Ivor Tsang, I. Tsang, 2021, ArXiv.

Dynamic vehicle routing with stochastic requests

Ivor W. Tsang, Dong Xu, Feiping Nie, 2003, IJCAI 2003.

A Flexible and Effective Linearization Method for Subspace Learning

Ivor W. Tsang, Feiping Nie, Changshui Zhang, 2013 .

Efficient Optimization of Performance Measures

Ivor W. Tsang, Zhi-Hua Zhou, Nan Li, 2013 .

Efficiently Learning Nonlinear Classifiers for Domain Specific Performance Measures pdf

Ivor W. Tsang, Zhi-Hua Zhou, Nan Li, 2010, ArXiv.

Co-labeling: A New Multi-view Learning Approach for Ambiguous Problems

Ivor W. Tsang, Dong Xu, Wen Li, 2012, 2012 IEEE 12th International Conference on Data Mining.

Batch mode Adaptive Multiple Instance Learning for computer vision tasks

Ivor W. Tsang, Dong Xu, Wen Li, 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

This article has been accepted for inclusion in a future issue of this journal. Content is final as presented, with the exception of pagination. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 1 Domain Adaptation from Multiple Sources: A Domain-

Ivor W. Tsang, Dong Xu, Lixin Duan, 2022 .

Improving Web Image Search by Bag-Based Reranking

Ivor W. Tsang, Dong Xu, Wen Li, 2011, IEEE Transactions on Image Processing.

Visual Event Recognition in Videos by Learning from Web Data

Ivor W. Tsang, Dong Xu, Jiebo Luo, 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Compressed K-Means for Large-Scale Clustering

Weiwei Liu, Ivor W. Tsang, Fumin Shen, 2017, AAAI.

Sparse Embedded k-Means Clustering

Weiwei Liu, Ivor W. Tsang, Xiao-Bo Shen, 2017, NIPS.

VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning

Lijun Zhang, Ivor W. Tsang, Licheng Jiao, 2018, IEEE Transactions on Knowledge and Data Engineering.

Memetic Search With Interdomain Learning: A Realization Between CVRP and CARP

Ivor W. Tsang, Liang Feng, Yew-Soon Ong, 2015, IEEE Transactions on Evolutionary Computation.

Memes as building blocks: a case study on evolutionary optimization + transfer learning for routing problems

Ivor W. Tsang, Ah-Hwee Tan, Liang Feng, 2015, Memetic Comput..

Meme as Building Block for Evolutionary Optimization of Problem Instances pdf

Ivor W. Tsang, Ah-Hwee Tan, Liang Feng, 2012, ArXiv.

An evolutionary search paradigm that learns with past experiences

Ivor W. Tsang, Ah-Hwee Tan, Liang Feng, 2012, 2012 IEEE Congress on Evolutionary Computation.

Robust Domain Adaptation for Relation Extraction via Clustering Consistency

Ivor W. Tsang, Hai Leong Chieu, Kian Ming Adam Chai, 2014, ACL.

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2018, ICML.

Hierarchical Maximum Margin Learning for Multi-Class Classification

Jian-Bo Yang, Ivor W. Tsang, I. Tsang, 2011, UAI.

A Feature Selection Method for Multivariate Performance Measures

Ivor W. Tsang, Qi Mao, I. Tsang, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Convex Method for Locating Regions of Interest with Multi-instance Learning

Ivor W. Tsang, Zhi-Hua Zhou, James T. Kwok, 2009, ECML/PKDD.

Multiview Alignment and Generation in CCA via Consistent Latent Encoding

Ivor W. Tsang, Donna Xu, Yaxin Shi, 2020, Neural Computation.

Fast and Low Memory Cost Matrix Factorization: Algorithm, Analysis, and Case Study

Chengqi Zhang, Ivor W. Tsang, Yan Yan, 2020, IEEE Transactions on Knowledge and Data Engineering.

Late Fusion via Subspace Search With Consistency Preservation

Ivor W. Tsang, Mingkui Tan, Yi Yang, 2019, IEEE Transactions on Image Processing.

Scalable Maximum Margin Matrix Factorization by Active Riemannian Subspace Search

Yi Yang, Chengqi Zhang, Ivor W. Tsang, 2015, IJCAI.

Rectified Encoder Network for High-Dimensional Imbalanced Learning

Wei-Jie Chen, Tao Zheng, Ivor Tsang, 2019, PRICAI.

How does Disagreement Help Generalization against Label Corruption?

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2019, ICML.

Co-teaching: Robust training of deep neural networks with extremely noisy labels

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2018, NeurIPS.

A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions

Ivor W. Tsang, Shutao Li, Mingkui Tan, 2011, IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics).

Co-Labeling for Multi-View Weakly Labeled Learning

Ivor W. Tsang, Dong Xu, Wen Li, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Robust Semi-Supervised Learning through Label Aggregation

Ivor W. Tsang, Yi Yang, Yan Yan, 2016, AAAI.

A Unified Feature Selection Framework for Graph Embedding on High Dimensional Data

Ivor W. Tsang, Mingkui Tan, Tat-Jen Cham, 2015, IEEE Transactions on Knowledge and Data Engineering.

Convex and scalable weakly labeled SVMs

Ivor W. Tsang, Zhi-Hua Zhou, James T. Kwok, 2013, J. Mach. Learn. Res..

Laplacian Embedded Regression for Scalable Manifold Regularization

Ivor W. Tsang, Dong Xu, Lin Chen, 2012, IEEE Transactions on Neural Networks and Learning Systems.

Transductive Ordinal Regression

Ivor W. Tsang, Yew-Soon Ong, Chun-Wei Seah, 2011, IEEE Transactions on Neural Networks and Learning Systems.

Textual Query of Personal Photos Facilitated by Large-Scale Web Data

Ivor W. Tsang, Dong Xu, Jiebo Luo, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Predictive Distribution Matching SVM for Multi-domain Learning

Ivor W. Tsang, Yew-Soon Ong, Gary Kee Khoon Lee, 2010, ECML/PKDD.

Domain Transfer SVM for video concept detection

Ivor W. Tsang, Dong Xu, Lixin Duan, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Domain adaptation from multiple sources via auxiliary classifiers

Ivor W. Tsang, Dong Xu, Tat-Seng Chua, 2009, ICML '09.

Large-Scale Sparsified Manifold Regularization

Ivor W. Tsang, James T. Kwok, I. Tsang, 2006, NIPS.

Large Margin Metric Learning for Multi-Label Prediction

Weiwei Liu, Ivor W. Tsang, I. Tsang, 2015, AAAI.

Efficient hyperkernel learning using second-order cone programming

Ivor W. Tsang, James T. Kwok, I. Tsang, 2004, IEEE Transactions on Neural Networks.

SVDD-Based Pattern Denoising

Ivor W. Tsang, James T. Kwok, Jongho Kim, 2007, Neural Computation.

A Class of Single-Class Minimax Probability Machines for Novelty Detection

Jacek M. Zurada, Ivor W. Tsang, James T. Kwok, 2007, IEEE Transactions on Neural Networks.

Learning the Kernel in Mahalanobis One-Class Support Vector Machines

Ivor W. Tsang, Shutao Li, James T. Kwok, 2006, The 2006 IEEE International Joint Conference on Neural Network Proceedings.

Generalized Core Vector Machines

Jacek M. Zurada, Ivor W. Tsang, James T. Kwok, 2006, IEEE Transactions on Neural Networks.

Very Large SVM Training using Core Vector Machines

Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung, 2005, AISTATS.

Scaling up support vector data description by using core-sets

J.T. Kwok, C.S. Chu, I.W. Tsang, 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).

White matter brain age as a biomarker of cerebrovascular burden in the ageing brain

I. Tsang, W. Wen, A. Thalamuthu, 2022, medRxiv.

Imitation Learning: Progress, Taxonomies and Opportunities pdf

Fang Chen, Jianlong Zhou, Sunny Verma, 2021, ArXiv.

SimpleNPKL: simple non-parametric kernel learning

Ivor W. Tsang, Steven C. H. Hoi, Jinfeng Zhuang, 2009, ICML '09.

Robust Plackett–Luce model for k-ary crowdsourced preferences

Ivor W. Tsang, Bo Han, Yuangang Pan, 2018, Machine Learning.

Efficient Batch Black-box Optimization with Deterministic Regret Bounds

I. Tsang, Yuan Yuan, Yueming Lyu, 2019, ArXiv.

The Emerging "Big Dimensionality"

Ivor W. Tsang, Yew-Soon Ong, Yiteng Zhai, 2014, IEEE Computational Intelligence Magazine.

Hyperspectral Imagery Classification via Stochastic HHSVMs

Weiwei Liu, Ivor W. Tsang, Bo Du, 2019, IEEE Transactions on Image Processing.

‘Who Likes What and, Why?’ Insights into Modeling Users’ Personality Based on Image ‘Likes’

Ivor W. Tsang, Sharath Chandra Guntuku, Weisi Lin, 2018, IEEE Transactions on Affective Computing.

Imitation Learning: Progress, Taxonomies and Challenges

I. Tsang, Jianlong Zhou, Fang Chen, 2021, IEEE Transactions on Neural Networks and Learning Systems.

DARER: Dual-task Temporal Relational Recurrent Reasoning Network for Joint Dialog Sentiment Classification and Act Recognition

I. Tsang, Bowen Xing, 2022, FINDINGS.

Pareto Rank Learning in Multi-objective Evolutionary Algorithms

Ivor W. Tsang, Yew-Soon Ong, Siwei Jiang, 2012, 2012 IEEE Congress on Evolutionary Computation.

Stochastic Multichannel Ranking with Brain Dynamics Preferences

Ivor W. Tsang, Chin-Teng Lin, Masashi Sugiyama, 2020, Neural Computation.

A Novel Regularized Principal Graph Learning Framework on Explicit Graph Representation pdf

Ivor W. Tsang, Li Wang, Qi Mao, 2015, ArXiv.

Hybrid Heterogeneous Transfer Learning through Deep Learning

Ivor W. Tsang, Yan Yan, Sinno Jialin Pan, 2014, AAAI.

Heterogeneous Domain Adaptation for Multiple Classes

Ivor W. Tsang, Mingkui Tan, Sinno Jialin Pan, 2014, AISTATS.

Learning with Augmented Features for Heterogeneous Domain Adaptation pdf

Ivor W. Tsang, Dong Xu, Lixin Duan, 2012, ICML.

The pre-image problem in kernel methods

Ivor W. Tsang, James T. Kwok, I. Tsang, 2003, IEEE Transactions on Neural Networks.

SC2Net: Sparse LSTMs for Sparse Coding

Ivor W. Tsang, Yong Liu, Hao Yang, 2018, AAAI.

Differential-Critic GAN: Generating What You Want by a Cue of Preferences

X. Yao, I. Tsang, Yuangang Pan, 2021, IEEE transactions on neural networks and learning systems.

Marginalized Average Attentional Network for Weakly-Supervised Learning pdf

Yueming Lyu, Ivor W. Tsang, Xi Shen, 2019, ICLR.

TRIP: Refining Image-to-Image Translation via Rival Preferences

Ivor W. Tsang, Xin Yao, Yuangang Pan, 2021, 2111.13411.

Auto-weighted multi-view co-clustering with bipartite graphs

Zenglin Xu, Ivor W. Tsang, Shudong Huang, 2020, Inf. Sci..

Learning word dependencies in text by means of a deep recurrent belief network

Ivor W. Tsang, Erik Cambria, Roy E. Welsch, 2016, Knowl. Based Syst..

Unfolded Self-Reconstruction LSH: Towards Machine Unlearning in Approximate Nearest Neighbour Search

Kim yong Tan, I. Tsang, Y. Ong, 2023, ArXiv.

Metric Learning for Multi-Output Tasks

Weiwei Liu, Ivor W. Tsang, Wenjie Zhang, 2019, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Safeguarded Dynamic Label Regression for Noisy Supervision

Jun Sun, Ivor W. Tsang, Ya Zhang, 2019, AAAI.

XAI Beyond Classification: Interpretable Neural Clustering

Joey Tianyi Zhou, I. Tsang, Hongyuan Zhu, 2018, J. Mach. Learn. Res..

Auto-weighted multi-view clustering via kernelized graph learning

Zenglin Xu, Zhao Kang, Ivor W. Tsang, 2019, Pattern Recognit..

Masking: A New Perspective of Noisy Supervision

Ivor W. Tsang, Gang Niu, Masashi Sugiyama, 2018, NeurIPS.

DEFEATnet—A Deep Conventional Image Representation for Image Classification

Ivor W. Tsang, Shenghua Gao, Lixin Duan, 2016, IEEE Transactions on Circuits and Systems for Video Technology.

The Emerging Trends of Multi-Label Learning

Ivor W. Tsang, Xiaobo Shen, Weiwei Liu, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Multilabel Prediction via Cross-View Search

Weiwei Liu, Ivor W. Tsang, Quan-Sen Sun, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Region-Based Saliency Detection and Its Application in Object Recognition

Liang-Tien Chia, Ivor W. Tsang, Shenghua Gao, 2014, IEEE Transactions on Circuits and Systems for Video Technology.

Face Hallucination With Finishing Touches

Ivor W. Tsang, Yang Zhang, Jun Li, 2020, IEEE Transactions on Image Processing.

Tag-Based Image Retrieval Improved by Augmented Features and Group-Based Refinement

Ivor W. Tsang, Dong Xu, Jiebo Luo, 2012, IEEE Transactions on Multimedia.

Curriculum Loss: Robust Learning and Generalization against Label Corruption pdf

Yueming Lyu, Ivor W. Tsang, I. Tsang, 2019, ICLR.

Learning Restoration is Not Enough: Transfering Identical Mapping for Single-Image Shadow Removal

I. Tsang, Qing Guo, Wei Feng, 2023, ArXiv.

Copy and Paste GAN: Face Hallucination From Shaded Thumbnails

Changhui Hu, Yang Zhang, Xin Yu, 2020, 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).

An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels

Weiwei Liu, Ivor W. Tsang, Klaus-Robert Müller, 2017, J. Mach. Learn. Res..

Making Decision Trees Feasible in Ultrahigh Feature and Label Dimensions

Weiwei Liu, Ivor W. Tsang, I. Tsang, 2017, J. Mach. Learn. Res..

Learning on Attribute-Missing Graphs

Siheng Chen, Jiangchao Yao, Xu Chen, 2020, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Leveraging Inpainting for Single-Image Shadow Removal

I. Tsang, Qing Guo, Wei Feng, 2023, ArXiv.

Structure-Informed Shadow Removal Networks

Rynson W. H. Lau, I. Tsang, Qing Guo, 2023, ArXiv.

How Does Disagreement Benefit Co-teaching?

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2019 .

Survey on Multi-Output Learning

Ivor W. Tsang, Donna Xu, Yew-Soon Ong, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Healing Sample Selection Bias by Source Classifier Selection

Ivor W. Tsang, Yew-Soon Ong, Chun-Wei Seah, 2011, 2011 IEEE 11th International Conference on Data Mining.

Towards ultrahigh dimensional feature selection for big data

Ivor W. Tsang, Mingkui Tan, Li Wang, 2012, J. Mach. Learn. Res..

A Survey of Label-noise Representation Learning: Past, Present and Future pdf

Ivor W. Tsang, James T. Kwok, Tongliang Liu, 2020, ArXiv.

Learning to Locate Relative Outliers

Ivor W. Tsang, Shukai Li, I. Tsang, 2011, ACML.

A unified probabilistic framework for robust manifold learning and embedding

Ivor W. Tsang, Li Wang, Qi Mao, 2017, Machine Learning.

Spectral Embedded Clustering: A Framework for In-Sample and Out-of-Sample Spectral Clustering

Ivor W. Tsang, Dong Xu, Feiping Nie, 2011, IEEE Transactions on Neural Networks.

Nonparametric Iterative Machine Teaching

I. Tsang, J. Kwok, Weiyang Liu, 2023, ICML.

UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translation

I. Tsang, Yaxin Shi, Ping Liu, 2023, ArXiv.

Millionaire: a hint-guided approach for crowdsourcing

Ivor W. Tsang, Qiang Yang, Masashi Sugiyama, 2018, Machine Learning.

Measuring Diversity in Graph Learning: A Unified Framework for Structured Multi-View Clustering

Zenglin Xu, Shudong Huang, Ivor Tsang, 2021, IEEE Transactions on Knowledge and Data Engineering.

I/O Efficient Approximate Nearest Neighbour Search based on Learned Functions

Ivor W. Tsang, Ying Zhang, Xuemin Lin, 2020, 2020 IEEE 36th International Conference on Data Engineering (ICDE).

Kernel Sparse Representation for Image Classification and Face Recognition

Liang-Tien Chia, Ivor W. Tsang, Shenghua Gao, 2010, ECCV.

CDD: Multi-view Subspace Clustering via Cross-view Diversity Detection

Zenglin Xu, Ivor W. Tsang, Shudong Huang, 2021, ACM Multimedia.

Transfer Ordinal Label Learning

Ivor W. Tsang, Yew-Soon Ong, Chun-Wei Seah, 2013, IEEE Transactions on Neural Networks and Learning Systems.

Learning Target Predictive Function without Target Labels

Ivor W. Tsang, Yew-Soon Ong, Qi Mao, 2012, 2012 IEEE 12th International Conference on Data Mining.

ST-Norm: Spatial and Temporal Normalization for Multi-variate Time Series Forecasting

Xuan Song, Ivor W. Tsang, Renhe Jiang, 2021, KDD.

Fast and Robust Rank Aggregation against Model Misspecification pdf

Ivor W. Tsang, Gang Niu, Masashi Sugiyama, 2019, ArXiv.

Incremental Subgraph Feature Selection for Graph Classification

Ivor W. Tsang, Xindong Wu, Ling Chen, 2017, IEEE Transactions on Knowledge and Data Engineering.

N-ary decomposition for multi-class classification

Klaus-Robert Müller, Shen-Shyang Ho, Joey Tianyi Zhou, 2019, Machine Learning.

Ivor W. Tsang, Dong Xu, Xinxing Xu, 2022 .

A Survey of Learning on Small Data: Generalization, Optimization, and Challenge

I. Tsang, J. Kwok, Min-Ling Zhang, 2022, 2207.14443.

A Multi-View Multi-Task Learning Framework for Multi-Variate Time Series Forecasting pdf

Ivor W. Tsang, Renhe Jiang, Jinliang Deng, 2021, IEEE Transactions on Knowledge and Data Engineering.

Graph Cross Networks with Vertex Infomax Pooling pdf

Ivor W. Tsang, Siheng Chen, Ya Zhang, 2020, NeurIPS.

Bayesian Active Learning by Disagreements: A Geometric Perspective

I. Tsang, Xiaofeng Cao, 2021, ArXiv.

Matching Pursuit LASSO Part I: Sparse Recovery Over Big Dictionary

Ivor W. Tsang, Mingkui Tan, Li Wang, 2015, IEEE Transactions on Signal Processing.

Matching Pursuit LASSO Part II: Applications and Sparse Recovery Over Batch Signals

Ivor W. Tsang, Mingkui Tan, Li Wang, 2013, IEEE Transactions on Signal Processing.

Familial Clustering for Weakly-Labeled Android Malware Using Hybrid Representation Learning

Zheng Zheng, Shirui Pan, Wanlei Zhou, 2020, IEEE Transactions on Information Forensics and Security.

Causal Intervention for Abstractive Related Work Generation

I. Tsang, Usman Naseem, Qi Zhang, 2023, EMNLP.

Diverse Preference Augmentation with Multiple Domains for Cold-start Recommendations

I. Tsang, Lixin Duan, Hongzhi Yin, 2022, 2022 IEEE 38th International Conference on Data Engineering (ICDE).

Deep Learning From Noisy Image Labels With Quality Embedding

Jun Sun, Chengqi Zhang, Ivor W. Tsang, 2017, IEEE Transactions on Image Processing.

Discovering Support and Affiliated Features from Very High Dimensions

Ivor W. Tsang, Mingkui Tan, Yew-Soon Ong, 2012, ICML.

Efficient Multitemplate Learning for Structured Prediction

Ivor W. Tsang, Qi Mao, I. Tsang, 2011, IEEE Transactions on Neural Networks and Learning Systems.

Deep Co-Image-Label Hashing for Multi-Label Image Retrieval

Ivor W. Tsang, Long Lan, Yuhui Zheng, 2021, IEEE Transactions on Multimedia.

Combating Negative Transfer From Predictive Distribution Differences

Ivor W. Tsang, Yew-Soon Ong, Chun-Wei Seah, 2013, IEEE Transactions on Cybernetics.

Zero-shot Learning with Complementary Attributes pdf

Ivor W. Tsang, Xiaofeng Xu, Chuancai Liu, 2018, ArXiv.

Vector Transport Free Riemannian LBFGS for Optimization on Symmetric Positive Deﬁnite Matrix Manifolds

I. Tsang, 2022 .

High-order Proximity Preserving Information Network Hashing

Xing Xie, Ivor W. Tsang, Longbing Cao, 2018, KDD.

A deep learning framework for Hybrid Heterogeneous Transfer Learning

Ivor W. Tsang, Sinno Jialin Pan, Joey Tianyi Zhou, 2019, Artif. Intell..

Matrix Co-completion for Multi-label Classification with Missing Features and Labels pdf

Ivor W. Tsang, Gang Niu, Miao Xu, 2018, ArXiv.

Transfer Learning for Cross-Language Text Categorization through Active Correspondences Construction

Ivor W. Tsang, Shen-Shyang Ho, Sinno Jialin Pan, 2016, AAAI.

Physics-inspired Learning for Structure-Aware Texture-Sensitive Underwater Image Enhancement

I. Tsang, Risheng Liu, Zexuan Li, 2021 .

Earning Extra Performance from Restrictive Feedbacks

I. Tsang, Yueming Lyu, Yuangang Pan, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

The Pulse of Urban Transport: Exploring the Co-evolving Pattern for Spatio-temporal Forecasting

Xuan Song, Ivor W. Tsang, Renhe Jiang, 2021, ACM Trans. Knowl. Discov. Data.

Large Margin Partial Label Machine

Jing Chai, Weijie Chen, Ivor W Tsang, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Parameter-Free Spectral Kernel Learning pdf

Ivor W. Tsang, Qi Mao, I. Tsang, 2010, UAI.

Degeneration in VAE: in the Light of Fisher Information Loss pdf

Ivor W. Tsang, Ya Zhang, Jiangchao Yao, 2018, ArXiv.

On the Optimality of Classifier Chain for Multi-label Classification

Weiwei Liu, Ivor W. Tsang, I. Tsang, 2015, NIPS.

Distribution Matching for Machine Teaching pdf

Ivor W. Tsang, Xiaofeng Cao, I. Tsang, 2021, IEEE transactions on neural networks and learning systems.

Multi-view Subspace Clustering on Topological Manifold

Zenglin Xu, I. Tsang, Yazhou Ren, 2022, NeurIPS.

Deep Pairwise Hashing for Cold-Start Recommendation pdf

Guowu Yang, Ivor W. Tsang, Yan Zhang, 2020, IEEE Transactions on Knowledge and Data Engineering.

Discrete Ranking-based Matrix Factorization with Self-Paced Learning

Yan Zhang, Guowu Yang, Ivor W. Tsang, 2018, KDD.

Minimax Sparse Logistic Regression for Very High-Dimensional Feature Selection

Ivor W. Tsang, Mingkui Tan, Li Wang, 2013, IEEE Transactions on Neural Networks and Learning Systems.

Learning Sparse SVM for Feature Selection on Very High Dimensional Datasets

Ivor W. Tsang, Mingkui Tan, Li Wang, 2010, ICML.

Contrastive Conditional Transport for Representation Learning pdf

Hongxia Yang, Jingren Zhou, Ivor Tsang, 2021, ArXiv.

A Convergence Path to Deep Learning on Noisy Labels

I. Tsang, Guowu Yang, Defu Liu, 2022, IEEE Transactions on Neural Networks and Learning Systems.

SuperInpaint: Learning Detail-Enhanced Attentional Implicit Representation for Super-resolutional Image Inpainting

I. Tsang, Qing Guo, Hongkai Yu, 2023, ArXiv.

Edge but not Least: Cross-View Graph Pooling pdf

Ivor W. Tsang, Jie Yin, Xiaowei Zhou, 2021, ECML/PKDD.

Automatic image tagging via category label and web data

Liang-Tien Chia, Ivor W. Tsang, Shenghua Gao, 2010, ACM Multimedia.

Two-Layer Multiple Kernel Learning

Ivor W. Tsang, Steven C. H. Hoi, Jinfeng Zhuang, 2011, AISTATS.

Incorporating the Loss Function Into Discriminative Clustering of Structured Outputs

Ivor W. Tsang, Leon Wenliang Zhong, James T. Kwok, 2010, IEEE Transactions on Neural Networks.

A Partial Label Metric Learning Algorithm for Class Imbalanced Data

I. Tsang, Rui-rui Zheng, Jie Chen, 2021 .

Concurrent Single-Label Image Classification and Annotation via Efficient Multi-Layer Group Sparse Coding

Liang-Tien Chia, Ivor W. Tsang, Shenghua Gao, 2014, IEEE Transactions on Multimedia.

Online Heterogeneous Transfer by Hedge Ensemble of Offline and Online Decisions

Ivor W. Tsang, Michael K. Ng, Qingyao Wu, 2018, IEEE Transactions on Neural Networks and Learning Systems.

Out of Context: A New Clue for Context Modeling of Aspect-based Sentiment Analysis

I. Tsang, Bowen Xing, 2021, J. Artif. Intell. Res..

Feature Weighting via Optimal Thresholding for Video Analysis

Nicu Sebe, Ivor W. Tsang, Yi Yang, 2013, 2013 IEEE International Conference on Computer Vision.

On the Convergence of a Family of Robust Losses for Stochastic Gradient Descent pdf

Ivor W. Tsang, Ling Chen, Bo Han, 2016, ECML/PKDD.

A Data-Analytics Approach for Enterprise Resilience

Ivor W. Tsang, Donna Xu, Eng K. Chew, 2019, IEEE Intelligent Systems.

Compact Multiple-Instance Learning

Weiwei Liu, Ivor W. Tsang, Jing Chai, 2017, CIKM.

Contrastive Attraction and Contrastive Repulsion for Representation Learning

I. Tsang, Mingyuan Zhou, Jingren Zhou, 2021, Trans. Mach. Learn. Res..

Relational Temporal Graph Reasoning for Dual-Task Dialogue Language Understanding

I. Tsang, Bowen Xing, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Co-evolving Graph Reasoning Network for Emotion-Cause Pair Extraction

I. Tsang, Bowen Xing, 2023, arXiv.org.

Beyond Majority Voting: A Coarse-to-Fine Label Filtration for Heavily Noisy Labels

Ling Chen, Bo Han, Joey Tianyi Zhou, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Multi-View Subspace Clustering by Joint Measuring of Consistency and Diversity

Zenglin Xu, I. Tsang, Shudong Huang, 2023, IEEE Transactions on Knowledge and Data Engineering.

Towards Equivalent Transformation of User Preferences in Cross Domain Recommendation

I. Tsang, Ya Zhang, Yuangang Pan, 2020, ArXiv.

Towards Faster Vehicle Routing by Transferring Knowledge From Customer Representation

Ivor W. Tsang, Abhishek Gupta, Yew-Soon Ong, 2022, IEEE Transactions on Intelligent Transportation Systems.

Making Trillion Correlations Feasible in Feature Grouping and Selection

Ivor W. Tsang, Yew-Soon Ong, Yiteng Zhai, 2016, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Toward Equivalent Transformation of User Preferences in Cross Domain Recommendation

I. Tsang, Ya Zhang, Yuangang Pan, 2022, ACM Trans. Inf. Syst..

Revisiting Weight Initialization of Deep Neural Networks

I. Tsang, M. Theobald, M. Skorski, 2021 .

Neural Subgraph Explorer: Reducing Noisy Information via Target-Oriented Syntax Graph Pruning

I. Tsang, Bowen Xing, 2022, IJCAI.

DigNet: Digging Clues from Local-Global Interactive Graph for Aspect-level Sentiment Classification

I. Tsang, Bowen Xing, 2022, ArXiv.

Understand Me, if You Refer to Aspect Knowledge: Knowledge-Aware Gated Recurrent Memory Network

I. Tsang, Bowen Xing, 2021, IEEE Transactions on Emerging Topics in Computational Intelligence.

Learning Structured Components: Towards Modular and Interpretable Multivariate Time Series Forecasting

I. Tsang, Yezhou Yang, Renhe Jiang, 2023, ArXiv.

Effectively Predicting Whether and When a Topic Will Become Prevalent in a Social Network

Weiwei Liu, Ivor W. Tsang, Frank Jiang, 2015, AAAI.

Open Set Domain Adaptation With Soft Unknown-Class Rejection

Lixin Duan, Yiming Xu, Ivor W Tsang, 2021, IEEE Transactions on Neural Networks and Learning Systems.

Latent Representation Guided Multi-View Clustering

Zenglin Xu, I. Tsang, Shudong Huang, 2023, IEEE Transactions on Knowledge and Data Engineering.

Efficient kernel feature extraction for massive data sets

Ivor W. Tsang, James T. Kwok, András Kocsor, 2006, KDD '06.

Earning Extra Performance from Restrictive Feedbacks

I. Tsang, Yueming Lyu, Yuangang Pan, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

A Structured Perspective of Volumes on Active Learning

Ivor W. Tsang, Guandong Xu, Xiaofeng Cao, 2018, Neurocomputing.

Geometric Active Learning via Enclosing Ball Boundary pdf

Jianliang Xu, Ivor W. Tsang, Guandong Xu, 2018, ArXiv.

CTS2: Time Series Smoothing with Constrained Reinforcement Learning

Ivor Tsang, Xin Liu, Yongshuai Liu, 2021, ACML.

Domain Adaption via Feature Selection on Explicit Feature Map

Amaury Lendasse, Wan-Yu Deng, Qing-Hua Zheng, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Sparse Perceptron Decision Tree for Millions of Dimensions

Weiwei Liu, Ivor W. Tsang, I. Tsang, 2016, AAAI.

Fast and Robust Rank Aggregation against Model Misspecification

Masashi Sugiyama, I. Tsang, Gang Niu, 2019, J. Mach. Learn. Res..

Stagewise learning for noisy k-ary preferences

I. Tsang, Bo Han, Yuangang Pan, 2018, Machine Learning.

Multi-Task Learning via Time-Aware Neural ODE

I. Tsang, Feiyang Ye, Yu Zhang, 2023, IJCAI.

Complementary Attributes: A New Clue to Zero-Shot Learning

I. Tsang, Chuancai Liu, Xiaofeng Xu, 2018, IEEE Transactions on Cybernetics.

Simple and Efficient Learning using Privileged Information pdf

Ivor W. Tsang, Yong Liu, Zheng Qin, 2016, ArXiv.

Principal Graph and Structure Learning Based on Reversed Graph Embedding.

Ivor W. Tsang, Yijun Sun, Li Wang, 2017, IEEE transactions on pattern analysis and machine intelligence.

Missingness-Pattern-Adaptive Learning With Incomplete Data

W. Liu, I. Tsang, Yilong Yin, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning Robust Node Representations on Graphs pdf

Xu Chen, Ya Zhang, Yuangang Pan, 2020, ArXiv.

Learning node representations against perturbations

I. Tsang, Ya Zhang, Yuangang Pan, 2020, Pattern Recognit..

Distribution Disagreement via Lorentzian Focal Representation

I. Tsang, Xiaofeng Cao, 2021, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Shattering Distribution for Active Learning

Xiaofeng Cao, Ivor W Tsang, I. Tsang, 2020, IEEE Transactions on Neural Networks and Learning Systems.

Long-short Distance Aggregation Networks for Positive Unlabeled Graph Learning

Ivor W. Tsang, Bo Du, Shirui Pan, 2019, CIKM.

Exploratory analysis of cell-based screening data for phenotype identification in drug-siRNA study

Ivor W. Tsang, Yew-Soon Ong, William-Chandra Tjhi, 2011, Int. J. Comput. Biol. Drug Des..

Domain Adaptation from Multiple Sources via Auxiliary Classifiers Tat-seng Chua

I. Tsang, Dong Xu, Lixin Duan, 2022 .

MetaCAR: Cross-Domain Meta-Augmentation for Content-Aware Recommendation

I. Tsang, Lixin Duan, Jie Shao, 2023, IEEE Transactions on Knowledge and Data Engineering.

Discovering User Interests from Social Images

Jun Sun, Ivor W. Tsang, Ya Zhang, 2017, MMM.

Location and Scatter Matching for Dataset Shift in Text Mining

Ivor W. Tsang, Bo Chen, Wai Lam, 2010, 2010 IEEE International Conference on Data Mining.

Extracting discriminative concepts for domain adaptation in text mining

Ivor W. Tsang, Bo Chen, Wai Lam, 2009, KDD.

Optimizing Multivariate Performance Measures from Multi-View Data

Ivor W. Tsang, Jim Jing-Yan Wang, Xin Gao, 2016, AAAI.

Event Detection Using Multi-level Relevance Labels and Multiple Features

Ivor W. Tsang, Yi Yang, Alexander G. Hauptmann, 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

Multi-view Positive and Unlabeled Learning

Ivor W. Tsang, Sinno Jialin Pan, Joey Tianyi Zhou, 2012, ACML.

Leveraging Inpainting for Single-Image Shadow Removal

I. Tsang, Qing Guo, Wei Feng, 2023, ArXiv.

Co-teaching: Robust training of deep neural networks with extremely noisy labels

Masashi Sugiyama, I. Tsang, Bo Han, 2018, NeurIPS.

Noisy Label Learning With Provable Consistency for a Wider Family of Losses

I. Tsang, Lixin Duan, Guowu Yang, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Latent Class-Conditional Noise Model

I. Tsang, Bo Han, Ya Zhang, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Safeguarded Dynamic Label Regression for Generalized Noisy Supervision

I. Tsang, Ya Zhang, Jiangchao Yao, 2019, ArXiv.

Pumpout: A Meta Approach to Robust Deep Learning with Noisy Labels

Xingrui Yu, Gang Niu, Miao Xu, 2018, 1809.11008.

Pumpout: A Meta Approach for Robustly Training Deep Neural Networks with Noisy Labels pdf

Xingrui Yu, Ivor W. Tsang, Gang Niu, 2018, ArXiv.

ERALIZATION AGAINST LABEL CORRUPTION

I. Tsang, Yueming Lyu, 2020 .

Safeguarded Dynamic Label Regression

I. Tsang, Ya Zhang, Jiangchao Yao, 2019 .

Probabilistic CCA with Implicit Distributions pdf

Donna Xu, Yaxin Shi, Yuangang Pan, 2019, ArXiv.

Transfer Hashing with Privileged Information

Ivor W. Tsang, Zheng Qin, Sinno Jialin Pan, 2016, IJCAI.

Maximum Margin/Volume Outlier Detection

Ivor W. Tsang, Shukai Li, I. Tsang, 2011, 2011 IEEE 23rd International Conference on Tools with Artificial Intelligence.

Relevance vector machine based infinite decision agent ensemble learning for credit risk analysis

Ivor W. Tsang, Narendra S. Chaudhari, Shukai Li, 2012, Expert Syst. Appl..

Online Mental Fatigue Monitoring via Indirect Brain Dynamics Evaluation

Yueming Lyu, Ivor W. Tsang, Yuangang Pan, 2021, Neural Computation.

Diversified SVM Ensembles for Large Data Sets

Ivor W. Tsang, James T. Kwok, András Kocsor, 2006, ECML.

Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data

Ivor W. Tsang, Mingkui Tan, Yan Yan, 2016, AAAI.

Online Product Quantization

Ivor W. Tsang, Donna Xu, Ying Zhang, 2017, IEEE Transactions on Knowledge and Data Engineering.

How does Disagreement Help Generalization against Label Corruption?

Masashi Sugiyama, I. Tsang, Bo Han, 2019, ICML.

Improving Generalization via Attribute Selection on Out-of-the-Box Data

Ivor W. Tsang, Xiaofeng Xu, Chuancai Liu, 2019, Neural Computation.

Is Matching Pursuit Solving Convex Problems?

I. Tsang, Mingkui Tan, Li Wang, 2013, arXiv.org.

Linear dependency between /spl epsi/ and the input noise in /spl epsi/-support vector regression.

I. Tsang, J. Kwok, 2003, IEEE transactions on neural networks.

Linear dependency between ε and the input noise in ε-support vector regression

I. Tsang, James T. Kwok, 2003, IEEE Trans. Neural Networks.

Towards Large-scale and Ultrahigh Dimensional Feature Selection via Feature Generation pdf

Ivor W. Tsang, Mingkui Tan, Li Wang, 2012, ArXiv.

Leveraging Open-Vocabulary Diffusion to Camouflaged Instance Segmentation

I. Tsang, Binh-Son Hua, Duc Thanh Nguyen, 2023, ArXiv.

An Empirical Study of Code Deobfuscations on Detecting Obfuscated Android Piggybacked Apps

I. Tsang, Tianqing Zhu, Zheng Zheng, 2020, 2020 27th Asia-Pacific Software Engineering Conference (APSEC).

A Survey on Multi-output Learning

I. Tsang, Y. Ong, Donna Xu, 2019, ArXiv.

Domain Adaptation for Coreference Resolution: An Adaptive Ensemble Approach

Jian-Bo Yang, Ivor W. Tsang, Hai Leong Chieu, 2012, EMNLP.

Understanding VAEs in Fisher-Shannon Plane pdf

Ivor W. Tsang, Jiangchao Yao, Huangjie Zheng, 2018, AAAI.

IRAD: Implicit Representation-driven Image Resampling against Adversarial Attacks

I. Tsang, Tianlin Li, Yue Cao, 2023, ArXiv.

Generalized Multiple Kernel Learning With Data-Dependent Priors

Ivor W. Tsang, Shenghua Gao, Li Wang, 2015, IEEE Transactions on Neural Networks and Learning Systems.

Laplacian Embedded Regression for Scalable Manifold Regularization

I. Tsang, Dong Xu, Lin Chen, 2012, IEEE Transactions on Neural Networks and Learning Systems.

Latent Smooth Skeleton Embedding

Ivor W. Tsang, Li Wang, Qi Mao, 2017, AAAI.

Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines

Ivor W. Tsang, James T. Kwok, András Kocsor, 2008, IEEE Transactions on Neural Networks.

Inferring Latent Network from Cascade Data for Dynamic Social Recommendation

Chengqi Zhang, Ivor W. Tsang, Jia Wu, 2016, 2016 IEEE 16th International Conference on Data Mining (ICDM).

Understanding Deep Representations Learned in Modeling Users Likes

Weisi Lin, Ivor W. Tsang, Sharath Chandra Guntuku, 2016, IEEE Transactions on Image Processing.

Deep Representations to Model User 'Likes'

Weisi Lin, Ivor W. Tsang, Sharath Chandra Guntuku, 2014, ACCV.

DifFormer: Multi-Resolutional Differencing Transformer With Dynamic Ranging for Time Series Analysis

Joey Tianyi Zhou, I. Tsang, Wen Wang, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Discovering Low-Rank Shared Concept Space for Adapting Text Mining Models

I. Tsang, Bo Chen, Tak-Lam Wong, 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Region-Based Saliency Detection and Its Application in Object Recognition

I. Tsang, Shenghua Gao, L. Chia, 2014, IEEE Transactions on Circuits and Systems for Video Technology.

Fusing images with different focuses using support vector machines

Ivor W. Tsang, Yaonan Wang, Shutao Li, 2004, IEEE Transactions on Neural Networks.

Graph Cross Networks with Vertex Infomax Pooling

I. Tsang, Siheng Chen, Ya Zhang, 2020, NeurIPS.

Learning Robust Node Representations on Graphs

I. Tsang, Ya Zhang, Yuangang Pan, 2020, ArXiv.

SIGUA: Forgetting May Make Learning with Noisy Labels More Robust

Masashi Sugiyama, I. Tsang, Bo Han, 2018, ICML.

Survey on Multi-Output Learning

I. Tsang, Chen Gong, Y. Ong, 2019, IEEE Transactions on Neural Networks and Learning Systems.

Covariance-Adaptive Sequential Black-box Optimization for Diffusion Targeted Generation

Kim yong Tan, I. Tsang, Yueming Lyu, 2024, 2406.00812.

Black-box Generalization of Machine Teaching

I. Tsang, Xiaofeng Cao, Yaming Guo, 2022, 2206.15205.

Second-Order Fine-Tuning without Pain for LLMs: A Hessian Informed Zeroth-Order Optimizer

I. Tsang, Yanjun Zhao, Yi Qian, 2024, ArXiv.

Combating Negative Transfer From Predictive Distribution Differences

Yew-Soon Ong, I. Tsang, Y. Ong, 2018 .

Co-Guiding for Multi-Intent Spoken Language Understanding

I. Tsang, Bowen Xing, 2023, IEEE Transactions on Pattern Analysis and Machine Intelligence.

USN: A Robust Imitation Learning Method against Diverse Action Noise

I. Tsang, Xingrui Yu, Bo Han, 2024, Journal of Artificial Intelligence Research.

Domain-Adversarial Network Alignment pdf

Xin Li, Huiting Hong, Yuangang Pan, 2019, IEEE Transactions on Knowledge and Data Engineering.

Spectral Embedded Hashing for Scalable Image Retrieval

Xuelong Li, Ivor W. Tsang, Dong Xu, 2014, IEEE Transactions on Cybernetics.

BGAE: Auto-Encoding Multi-View Bipartite Graph Clustering

I. Tsang, Yuangang Pan, Jie Liu, 2024, IEEE Transactions on Knowledge and Data Engineering.

Learning Efficient Planning-based Rewards for Imitation Learning

I. Tsang, Xingrui Yu, Yueming Lyu, 2021 .

Subgroup-based Rank-1 Lattice Quasi-Monte Carlo pdf

Yueming Lyu, Ivor W. Tsang, Yuan Yuan, 2020, NeurIPS.

Label Embedding with Partial Heterogeneous Contexts

Ivor W. Tsang, Donna Xu, Shirui Pan, 2018, AAAI.

Doubly Approximate Nearest Neighbor Classification

Weiwei Liu, Ivor W. Tsang, Xuemin Lin, 2018, AAAI.