Experimental analysis of input weight freezing in constructive neural networks. - James Tin-Yau Kwok; Dit-Yan Yeung (ICNN, 1993)

Efficient cross-validation for feedforward neural networks. - James Tin-Yau Kwok; Dit-Yan Yeung (ICNN, 1995)

Improving the approximation and convergence capabilities of projection pursuit learning. - James Tin-Yau Kwok; Dit-Yan Yeung (Neural Process. Lett., 1995)

Bayesian Regularization in Constructive Neural Networks. - James Tin-Yau Kwok; Dit-Yan Yeung (ICANN, 1996)

Use of bias term in projection pursuit learning improves approximation and convergence properties. - James Tin-Yau Kwok; Dit-Yan Yeung (IEEE Trans. Neural Networks, 1996)

Constructive algorithms for structure learning in feedforward neural networks for regression problems. - James Tin-Yau Kwok; Dit-Yan Yeung (IEEE Trans. Neural Networks, 1997)

Objective functions for training new hidden units in constructive neural networks. - James Tin-Yau Kwok; Dit-Yan Yeung (IEEE Trans. Neural Networks, 1997)

Automated Text Categorization Using Support Vector Machine. - James Tin-Yau Kwok (ICONIP, 1998)

Support vector mixture for classification and regression problems. - James Tin-Yau Kwok (ICPR, 1998)

Integrating the evidence framework and the support vector machine. - James Tin-Yau Kwok (ESANN, 1999)

Moderating the outputs of support vector machine classifiers. - James Tin-Yau Kwok (IEEE Trans. Neural Networks, 1999)

Rival Penalized Competitive Learning for Model-Based Sequence Clustering. - Martin H. C. Law; James T. Kwok (ICPR, 2000)

The evidence framework applied to support vector machines. - James Tin-Yau Kwok (IEEE Trans. Neural Networks Learn. Syst., 2000)

Bayesian Support Vector Regression. - Martin H. C. Law; James Tin-Yau Kwok (AISTATS, 2001)

Applying the Bayesian Evidence Framework to nu -Support Vector Regression. - Martin H. C. Law; James T. Kwok (ECML, 2001)

Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression. - James T. Kwok (ICANN, 2001)

Combination of images with diverse focuses using the spatial frequency. - Shutao Li; James T. Kwok; Yaonan Wang 001 (Inf. Fusion, 2001)

Fusing Images with Multiple Focuses Using Support Vector Machines. - Shutao Li; James T. Kwok; Yaonan Wang 001 (ICANN, 2002)

Improving De-Noising by Coefficient De-Noising and Dyadic Wavelet Transform. - Hailong Zhu; James T. Kwok; Liangsheng Qu (ICPR (2), 2002)

Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. - Shutao Li; James T. Kwok; Yaonan Wang 001 (Inf. Fusion, 2002)

Multifocus image fusion using artificial neural networks. - Shutao Li; James T. Kwok; Yaonan Wang 001 (Pattern Recognit. Lett., 2002)

Learning with Idealized Kernels. - James T. Kwok; Ivor W. Tsang (ICML, 2003)

Parametric Distance Metric Learning with Label Information. - Zhihua Zhang 004; James T. Kwok; Dit-Yan Yeung (IJCAI, 2003)

Eigenvoice Speaker Adaptation via Composite Kernel PCA. - James T. Kwok; Brian Mak; Simon Ka-Lung Ho (NIPS, 2003)

Mining customer product ratings for personalized marketing. - Kwok-Wai Cheung 001; James T. Kwok; Martin H. C. Law; Kwok Ching Tsui (Decis. Support Syst., 2003)

Texture classification using the support vector machines. - Shutao Li; James T. Kwok; Hailong Zhu; Yaonan Wang 001 (Pattern Recognit., 2003)

Linear dependency between ε and the input noise in ε-support vector regression. - James T. Kwok; Ivor W. Tsang (IEEE Trans. Neural Networks, 2003)

Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo. - Zhihua Zhang 004; Kap Luk Chan; James T. Kwok; Dit-Yan Yeung (AAAI, 2004)

Incremental PCA based face recognition. - Haitao Zhao 002; Pong Chi Yuen; James T. Kwok; Jingyu Yang (ICARCV, 2004)

A study of various composite kernels for kernel eigenvoice speaker adaptation. - Brian Mak; James T. Kwok; Simon Ka-Lung Ho (ICASSP (1), 2004)

Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model. - Zhihua Zhang 004; James T. Kwok; Dit-Yan Yeung (ICML, 2004)

Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm. - Zhihua Zhang 004; Dit-Yan Yeung; James T. Kwok (ICML, 2004)

Scaling up support vector data description by using core-sets. - Calvin S. Chu; Ivor W. Tsang; James T. Kwok (IJCNN, 2004)

Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA. - Brian Mak; Simon Ka-Lung Ho; James T. Kwok (INTERSPEECH, 2004)

Dissimilarity learning for nominal data. - Victor Cheng; Chun Hung Li; James T. Kwok; Chi-Kwong Li (Pattern Recognit., 2004)

The pre-image problem in kernel methods. - James Tin-Yau Kwok; Ivor Wai-Hung Tsang (IEEE Trans. Neural Networks, 2004)

Fusing images with different focuses using support vector machines. - Shutao Li; James Tin-Yau Kwok; Ivor Wai-Hung Tsang; Yaonan Wang 001 (IEEE Trans. Neural Networks, 2004)

Towards end-to-end privacy control in the outsourcing of marketing activities: a web service integration solution. - Patrick C. K. Hung; Dickson K. W. Chiu; W. W. Fung; William K. Cheung 001; Raymond K. Wong 001; Samuel P. M. Choi; Eleanna Kafeza; James T. Kwok; Joshua C. C. Pun; Vivying S. Y. Cheng (ICEC, 2005)

Very Large SVM Training using Core Vector Machines. - Ivor W. Tsang; James Tin-Yau Kwok; Pak-Ming Cheung (AISTATS, 2005)

Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation. - Kai Zhang 001; Ming Tang 001; James T. Kwok (CVPR (2), 2005)

Position estimation for wireless sensor networks. - Kin Fung Simon Wong; Ivor W. Tsang; Victor Cheung; S.-H. Gary Chan; James T. Kwok (GLOBECOM, 2005)

Core Vector Regression for very large regression problems. - Ivor W. Tsang; James T. Kwok; Kimo T. Lai (ICML, 2005)

Accurate and Low-cost Location Estimation Using Kernels. - Jeffrey Junfeng Pan; James T. Kwok; Qiang Yang 001; Yiqiang Chen 001 (IJCAI, 2005)

Pattern de-noising based on support vector data description. - Jooyoung Park 001; Daesung Kang; Jongho Kim; James Tin-Yau Kwok; Ivor Wai-Hung Tsang (IJCNN, 2005)

Kernel relevant component analysis for distance metric learning. - Ivor Wai-Hung Tsang; Pak-Ming Cheung; James Tin-Yau Kwok (IJCNN, 2005)

Data-dependent kernels for high-dimensional data classification. - Jingdong Wang 001; James T. Kwok; Helen C. Shen; Long Quan (IJCNN, 2005)

Core Vector Machines: Fast SVM Training on Very Large Data Sets. - Ivor W. Tsang; James T. Kwok; Pak-Ming Cheung (J. Mach. Learn. Res., 2005)

Kernel Eigenvoice Speaker Adaptation. - Brian Mak; James Tin-Yau Kwok; Simon Ka-Lung Ho (IEEE Trans. Speech Audio Process., 2005)

Accelerated Convergence Using Dynamic Mean Shift. - Kai Zhang 001; James T. Kwok; Ming Tang 001 (ECCV (2), 2006)

Diversified SVM Ensembles for Large Data Sets. - Ivor W. Tsang; András Kocsor; James T. Kwok (ECML, 2006)

Fast Speaker Adaption Via Maximum Penalized Likelihood Kernel Regression. - Ivor W. Tsang; James T. Kwok; Brian Mak; Kai Zhang 001; Jeffrey Junfeng Pan (ICASSP (1), 2006)

Facial Image Reconstruction by SVDD-Based Pattern De-noising. - Jooyoung Park 001; Daesung Kang; James T. Kwok; Sang-Woong Lee; Bon-Woo Hwang; Seong-Whan Lee (ICB, 2006)

A regularization framework for multiple-instance learning. - Pak-Ming Cheung; James T. Kwok (ICML, 2006)

Locally adaptive classification piloted by uncertainty. - Juan Dai; Shuicheng Yan; Xiaoou Tang; James T. Kwok (ICML, 2006)

Block-quantized kernel matrix for fast spectral embedding. - Kai Zhang 001; James T. Kwok (ICML, 2006)

Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares. - Shutao Li; Chen Liao; James T. Kwok (ICONIP (3), 2006)

Multimodal Registration using the Discrete Wavelet Frame Transform. - Shutao Li; Jinglin Peng; James T. Kwok; Jing Zhang 037 (ICPR (3), 2006)

Efficient Classification of Multi-label and Imbalanced Data using Min-Max Modular Classifiers. - Ken Chen; Bao-Liang Lu; James T. Kwok (IJCNN, 2006)

Wavelet-Based Feature Extraction for Microarray Data Classification. - Shutao Li; Chen Liao; James T. Kwok (IJCNN, 2006)

Learning the Kernel in Mahalanobis One-Class Support Vector Machines. - Ivor W. Tsang; James T. Kwok; Shutao Li (IJCNN, 2006)

Efficient kernel feature extraction for massive data sets. - Ivor W. Tsang; András Kocsor; James T. Kwok (KDD, 2006)

Large-Scale Sparsified Manifold Regularization. - Ivor W. Tsang; James T. Kwok (NIPS, 2006)

Model-based transductive learning of the kernel matrix. - Zhihua Zhang 004; James T. Kwok; Dit-Yan Yeung (Mach. Learn., 2006)

Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting. - Brian Kan-Wing Mak; Roger Wend-Huu Hsiao; Simon Ka-Lung Ho; James T. Kwok (IEEE Trans. Speech Audio Process., 2006)

Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing. - Jeffrey Junfeng Pan; James T. Kwok; Qiang Yang 001; Yiqiang Chen 001 (IEEE Trans. Knowl. Data Eng., 2006)

Efficient hyperkernel learning using second-order cone programming. - Ivor Wai-Hung Tsang; James Tin-Yau Kwok (IEEE Trans. Neural Networks, 2006)

Generalized Core Vector Machines. - Ivor Wai-Hung Tsang; James Tin-Yau Kwok; Jacek M. Zurada (IEEE Trans. Neural Networks, 2006)

A novel incremental principal component analysis and its application for face recognition. - Haitao Zhao 002; Pong Chi Yuen; James T. Kwok (IEEE Trans. Syst. Man Cybern. Part B, 2006)

Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning. - Sinno Jialin Pan; James T. Kwok; Qiang Yang 001; Jeffrey Junfeng Pan (AAAI, 2007)

Simpler core vector machines with enclosing balls. - Ivor W. Tsang; András Kocsor; James T. Kwok (ICML, 2007)

Marginalized Multi-Instance Kernels. - James T. Kwok; Pak-Ming Cheung (IJCAI, 2007)

Ensembles of Partially Trained SVMs with Multiplicative Updates. - Ivor W. Tsang; James T. Kwok (IJCAI, 2007)

End-to-end privacy control in service outsourcing of human intensive processes: A multi-layered Web service integration approach. - Patrick C. K. Hung; Dickson K. W. Chiu; W. W. Fung; William K. Cheung 001; Raymond K. Wong 001; Samuel P. M. Choi; Eleanna Kafeza; James T. Kwok; Joshua C. C. Pun; Vivying S. Y. Cheng (Inf. Syst. Frontiers, 2007)

Surrogate maximization/minimization algorithms and extensions. - Zhihua Zhang 004; James T. Kwok; Dit-Yan Yeung (Mach. Learn., 2007)

SVDD-Based Pattern Denoising. - Jooyoung Park 001; Daesung Kang; Jongho Kim; James T. Kwok; Ivor W. Tsang (Neural Comput., 2007)

Face recognition using spectral features. - Fei Wang 001; Jingdong Wang 001; Changshui Zhang; James T. Kwok (Pattern Recognit., 2007)

A Class of Single-Class Minimax Probability Machines for Novelty Detection. - James T. Kwok; Ivor Wai-Hung Tsang; Jacek M. Zurada (IEEE Trans. Neural Networks, 2007)

Transfer Learning via Dimensionality Reduction. - Sinno Jialin Pan; James T. Kwok; Qiang Yang 001 (AAAI, 2008)

Transferring Localization Models across Space. - Sinno Jialin Pan; Dou Shen; Qiang Yang 001; James T. Kwok (AAAI, 2008)

Improved Nyström low-rank approximation and error analysis. - Kai Zhang 001; Ivor W. Tsang; James T. Kwok (ICML, 2008)

Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines. - Ivor Wai-Hung Tsang; András Kocsor; James Tin-Yau Kwok (IEEE Trans. Neural Networks, 2008)

Matrix-Variate Factor Analysis and Its Applications. - Xianchao Xie; Shuicheng Yan; James T. Kwok; Thomas S. Huang (IEEE Trans. Neural Networks, 2008)

Unsupervised Maximum Margin Feature Selection with manifold regularization. - Bin Zhao 004; James Tin-Yau Kwok; Fei Wang 001; Changshui Zhang (CVPR, 2009)

Accelerated Gradient Method for Multi-task Sparse Learning Problem. - Xi Chen 010; Weike Pan; James T. Kwok; Jaime G. Carbonell (ICDM, 2009)

Maximum Margin Clustering with Multivariate Loss Function. - Bin Zhao 004; James Tin-Yau Kwok; Changshui Zhang (ICDM, 2009)

Semi-supervised learning using label mean. - Yufeng Li 008; James T. Kwok; Zhi-Hua Zhou (ICML, 2009)

Prototype vector machine for large scale semi-supervised learning. - Kai Zhang 001; James T. Kwok; Bahram Parvin (ICML, 2009)

Accelerated Gradient Methods for Stochastic Optimization and Online Learning. - Chonghai Hu; James T. Kwok; Weike Pan (NIPS, 2009)

A Convex Method for Locating Regions of Interest with Multi-instance Learning. - Yufeng Li 008; James T. Kwok; Ivor W. Tsang; Zhi-Hua Zhou (ECML/PKDD (2), 2009)

Multiple Kernel Clustering. - Bin Zhao 004; James T. Kwok; Changshui Zhang (SDM, 2009)

Tighter and Convex Maximum Margin Clustering. - Yufeng Li 008; Ivor W. Tsang; James Tin-Yau Kwok; Zhi-Hua Zhou (AISTATS, 2009)

Density-Weighted Nyström Method for Computing Large Kernel Eigensystems. - Kai Zhang 001; James T. Kwok (Neural Comput., 2009)

Maximum Penalized Likelihood Kernel Regression for Fast Adaptation. - Brian Kan-Wing Mak; Tsz-Chung Lai; Ivor W. Tsang; James Tin-Yau Kwok (IEEE Trans. Speech Audio Process., 2009)

Building Sparse Multiple-Kernel SVM Classifiers. - Mingqing Hu; Yiqiang Chen 001; James Tin-Yau Kwok (IEEE Trans. Neural Networks, 2009)

Maximum Margin Clustering Made Practical. - Kai Zhang 001; Ivor W. Tsang; James T. Kwok (IEEE Trans. Neural Networks, 2009)

Cost-Sensitive Semi-Supervised Support Vector Machine. - Yufeng Li 008; James T. Kwok; Zhi-Hua Zhou (AAAI, 2010)

Online multiple instance learning with no regret. - Mu Li 001; James T. Kwok; Bao-Liang Lu (CVPR, 2010)

Making Large-Scale Nyström Approximation Possible. - Mu Li 001; James T. Kwok; Bao-Liang Lu (ICML, 2010)

Manifold regularization for structured outputs via the joint kernel. - Chonghai Hu; James T. Kwok (IJCNN, 2010)

Spectral and Semidefinite Relaxation of the CLUHSIC Algorithm. - Wen-Yun Yang; James T. Kwok; Bao-Liang Lu (SDM, 2010)

Text detection in images using sparse representation with discriminative dictionaries. - Ming Zhao; Shutao Li; James Tin-Yau Kwok (Image Vis. Comput., 2010)

Fast and accurate kernel density approximation using a divide-and-conquer approach. - Yan-xia Jin; Kai Zhang 001; James T. Kwok; Han-chang Zhou (J. Zhejiang Univ. Sci. C, 2010)

Simplifying mixture models through function approximation. - Kai Zhang 001; James T. Kwok (IEEE Trans. Neural Networks, 2010)

Clustered Nyström method for large scale manifold learning and dimension reduction. - Kai Zhang 001; James T. Kwok (IEEE Trans. Neural Networks, 2010)

Incorporating the loss function into discriminative clustering of structured outputs. - Wenliang Zhong; Weike Pan; James T. Kwok; Ivor W. Tsang (IEEE Trans. Neural Networks, 2010)

Time and space efficient spectral clustering via column sampling. - Mu Li 001; Xiao-Chen Lian; James T. Kwok; Bao-Liang Lu (CVPR, 2011)

MultiLabel Classification on Tree- and DAG-Structured Hierarchies. - Wei Bi; James T. Kwok (ICML, 2011)

Structured clustering with automatic kernel adaptation. - Weike Pan; James T. Kwok (IJCNN, 2011)

Incorporating cellular sorting structure for better prediction of protein subcellular locations. - Wen-Yun Yang; Bao-Liang Lu; James T. Kwok (J. Exp. Theor. Artif. Intell., 2011)

Domain Adaptation via Transfer Component Analysis. - Sinno Jialin Pan; Ivor W. Tsang; James T. Kwok; Qiang Yang 001 (IEEE Trans. Neural Networks, 2011)

A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions. - Shutao Li; Mingkui Tan; Ivor W. Tsang; James Tin-Yau Kwok (IEEE Trans. Syst. Man Cybern. Part B, 2011)

Hierarchical Multilabel Classification with Minimum Bayes Risk. - Wei Bi; James T. Kwok (ICDM, 2012)

Convex Multitask Learning with Flexible Task Clusters. - Wenliang Zhong; James Tin-Yau Kwok (ICML, 2012)

A brief introduction to the special issue for ISNN2010. - Liqing Zhang 001; James Tin-Yau Kwok; Changshui Zhang (Neurocomputing, 2012)

Bilinear Probabilistic Principal Component Analysis. - Jianhua Zhao; Philip L. H. Yu; James T. Kwok (IEEE Trans. Neural Networks Learn. Syst., 2012)

Efficient Sparse Modeling With Automatic Feature Grouping. - Leon Wenliang Zhong; James T. Kwok (IEEE Trans. Neural Networks Learn. Syst., 2012)

Learning from High-Dimensional Data in Multitask/Multilabel Classification. - James T. Kwok (ACPR, 2013)

Efficient Learning for Models with DAG-Structured Parameter Constraints. - Leon Wenliang Zhong; James T. Kwok (ICDM, 2013)

Efficient Multi-label Classification with Many Labels. - Wei Bi; James Tin-Yau Kwok (ICML (3), 2013)

Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels. - Kai Zhang 001; Vincent Wenchen Zheng; Qiaojun Wang; James Tin-Yau Kwok; Qiang Yang 001; Ivan Marsic (ICML (3), 2013)

Flexible Nonparametric Kernel Learning with Different Loss Functions. - En-Liang Hu; James T. Kwok (ICONIP (2), 2013)

Efficient Kernel Learning from Side Information Using ADMM. - En-Liang Hu; James T. Kwok (IJCAI, 2013)

Accurate Probability Calibration for Multiple Classifiers. - Wenliang Zhong; James T. Kwok (IJCAI, 2013)

Convex and scalable weakly labeled SVMs. - Yufeng Li 008; Ivor W. Tsang; James T. Kwok; Zhi-Hua Zhou (J. Mach. Learn. Res., 2013)

Multilabel Classification with Label Correlations and Missing Labels. - Wei Bi; James T. Kwok (AAAI, 2014)

Accurate Integration of Aerosol Predictions by Smoothing on a Manifold. - Shuai Zheng 004; James T. Kwok (AAAI, 2014)

Gradient Descent with Proximal Average for Nonconvex and Composite Regularization. - Wenliang Zhong; James T. Kwok (AAAI, 2014)

Accelerated Stochastic Gradient Method for Composite Regularization. - Wenliang Zhong; James Tin-Yau Kwok (AISTATS, 2014)

Asynchronous Distributed ADMM for Consensus Optimization. - Ruiliang Zhang; James T. Kwok (ICML, 2014)

Fast Stochastic Alternating Direction Method of Multipliers. - Wenliang Zhong; James Tin-Yau Kwok (ICML, 2014)

Learning to Predict from Crowdsourced Data. - Wei Bi; Liwei Wang 009; James T. Kwok; Zhuowen Tu (UAI, 2014)

Selected papers from the 2011 International Conference on Neural Information Processing (ICONIP 2011). - James T. Kwok; Liqing Zhang 001; Hongtao Lu (Neurocomputing, 2014)

Simple randomized algorithms for online learning with kernels. - Wenwu He; James T. Kwok (Neural Networks, 2014)

Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification. - Wei Bi; James T. Kwok (IEEE Trans. Neural Networks Learn. Syst., 2014)

Colorization by Patch-Based Local Low-Rank Matrix Completion. - Quanming Yao; James T. Kwok (AAAI, 2015)

Fast Low-Rank Matrix Learning with Nonconvex Regularization. - Quanming Yao; James T. Kwok; Wenliang Zhong (ICDM, 2015)

Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion. - Quanming Yao; James T. Kwok (IJCAI, 2015)

Collaborative filtering via co-factorization of individuals and groups. - Yihai Huang; James T. Kwok (IJCNN, 2015)

Fast Second Order Stochastic Backpropagation for Variational Inference. - Kai Fan 002; Ziteng Wang; Jeffrey M. Beck; James T. Kwok; Katherine A. Heller (NIPS, 2015)

Fast Distributed Asynchronous SGD with Variance Reduction. - Ruiliang Zhang; Shuai Zheng 004; James T. Kwok (CoRR, 2015)

Bayes-Optimal Hierarchical Multilabel Classification. - Wei Bi; James T. Kwok (IEEE Trans. Knowl. Data Eng., 2015)

Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent. - En-Liang Hu; James T. Kwok (IEEE Trans. Neural Networks Learn. Syst., 2015)

Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVD. - Mu Li 001; Wei Bi; James T. Kwok; Bao-Liang Lu (IEEE Trans. Neural Networks Learn. Syst., 2015)

Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines. - Kai Zhang 001; Liang Lan; James T. Kwok; Slobodan Vucetic; Bahram Parvin (IEEE Trans. Neural Networks Learn. Syst., 2015)

Machine Learning. - James Tin-Yau Kwok; Zhi-Hua Zhou; Lei Xu 001 (Handbook of Computational Intelligence, 2015)

Fast Nonsmooth Regularized Risk Minimization with Continuation. - Shuai Zheng 004; Ruiliang Zhang; James T. Kwok (AAAI, 2016)

Efficient Learning of Timeseries Shapelets. - Lu Hou 002; James T. Kwok; Jacek M. Zurada (AAAI, 2016)

Towards Safe Semi-Supervised Learning for Multivariate Performance Measures. - Yufeng Li 008; James T. Kwok; Zhi-Hua Zhou (AAAI, 2016)

Asynchronous Distributed Semi-Stochastic Gradient Optimization. - Ruiliang Zhang; Shuai Zheng 004; James T. Kwok (AAAI, 2016)

Fast-and-Light Stochastic ADMM. - Shuai Zheng 004; James T. Kwok (IJCAI, 2016)

Greedy Learning of Generalized Low-Rank Models. - Quanming Yao; James T. Kwok (IJCAI, 2016)

Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering. - Xiawei Guo; James T. Kwok (ECML/PKDD (1), 2016)

Learning of Generalized Low-Rank Models: A Greedy Approach. - Quanming Yao; James T. Kwok (CoRR, 2016)

Fast Learning with Nonconvex L1-2 Regularization. - Quanming Yao; James T. Kwok (CoRR, 2016)

Special issue: First International Conference on Big Data and Smart Computing (BigComp2014). - Jinho Kim; James T. Kwok; Kazutoshi Sumiya; Byoung-Tak Zhang (Data Knowl. Eng., 2016)

Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm. - Xiawei Guo; Quanming Yao; James Tin-Yau Kwok (AAAI, 2017)

Collaborative Filtering with Social Local Models. - Huan Zhao 002; Quanming Yao; James T. Kwok; Dik Lun Lee (ICDM, 2017)

Loss-aware Binarization of Deep Networks. - Lu Hou 002; Quanming Yao; James T. Kwok (ICLR (Poster), 2017)

Follow the Moving Leader in Deep Learning. - Shuai Zheng 004; James T. Kwok (ICML, 2017)

Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems. - Quanming Yao; James T. Kwok; Fei Gao 018; Wei Chen 034; Tie-Yan Liu (IJCAI, 2017)

Zero-shot learning with a partial set of observed attributes. - Yaqing Wang 002; James T. Kwok; Quanming Yao; Lionel M. Ni (IJCNN, 2017)

Online Convolutional Sparse Coding. - Yaqing Wang 002; Quanming Yao; James T. Kwok; Lionel M. Ni (CoRR, 2017)

Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. - Quanming Yao; James T. Kwok (J. Mach. Learn. Res., 2017)

Multi-Label learning in the independent label sub-spaces. - Elham J. Barezi; James T. Kwok; Hamid R. Rabiee (Pattern Recognit. Lett., 2017)

A Note on the Unification of Adaptive Online Learning. - Wenwu He; James Tin-Yau Kwok; Ji Zhu; Yang Liu 018 (IEEE Trans. Neural Networks Learn. Syst., 2017)

Loss-aware Weight Quantization of Deep Networks. - Lu Hou 002; James T. Kwok (ICLR (Poster), 2018)

Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data. - Shuai Zheng 004; James Tin-Yau Kwok (ICML, 2018)

Online Convolutional Sparse Coding with Sample-Dependent Dictionary. - Yaqing Wang 002; Quanming Yao; James Tin-Yau Kwok; Lionel M. Ni (ICML, 2018)

Scalable Robust Matrix Factorization with Nonconvex Loss. - Quanming Yao; James T. Kwok (NeurIPS, 2018)

Learning with Heterogeneous Side Information Fusion for Recommender Systems. - Huan Zhao 002; Quanming Yao; Yangqiu Song; James T. Kwok; Dik Lun Lee (CoRR, 2018)

Power Law in Sparsified Deep Neural Networks. - Lu Hou 002; James T. Kwok (CoRR, 2018)

Corrigendum to "Multi-label learning in the independent label sub-spaces" [Pattern Recognition Letters 97(2017) 8-12]. - Elham J. Barezi; James T. Kwok; Hamid R. Rabiee (Pattern Recognit. Lett., 2018)

Scalable Online Convolutional Sparse Coding. - Yaqing Wang 002; Quanming Yao; James T. Kwok; Lionel M. Ni (IEEE Trans. Image Process., 2018)

Multi-Label Learning with Global and Local Label Correlation. - Yue Zhu 001; James T. Kwok; Zhi-Hua Zhou (IEEE Trans. Knowl. Data Eng., 2018)

Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set. - Yuefeng Ma; Xun Liang 001; James T. Kwok; Jianping Li 001; Xiaoping Zhou; Haiyan Zhang (IEEE Trans. Neural Networks Learn. Syst., 2018)

Analysis of Quantized Models. - Lu Hou 002; Ruiliang Zhang; James T. Kwok (ICLR (Poster), 2019)

Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. - Quanming Yao; James Tin-Yau Kwok; Bo Han 003 (ICML, 2019)

Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. - Quanming Yao; Xiawei Guo; James T. Kwok; Wei-Wei Tu; Yuqiang Chen; Wenyuan Dai; Qiang Yang 001 (IJCAI, 2019)

Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration. - Minsam Kim; James T. Kwok (IJCNN, 2019)

Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. - Shuai Zheng 004; Ziyue Huang; James T. Kwok (NeurIPS, 2019)

Normalization Helps Training of Quantized LSTM. - Lu Hou 002; Jinhua Zhu 001; James T. Kwok; Fei Gao 018; Tao Qin 001; Tie-Yan Liu (NeurIPS, 2019)

Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space. - Zac Wellmer; James T. Kwok (ECML/PKDD (3), 2019)

General Convolutional Sparse Coding with Unknown Noise. - Yaqing Wang 002; James T. Kwok; Lionel M. Ni (CoRR, 2019)

Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning. - Shuai Zheng 004; James T. Kwok (CoRR, 2019)

Searching for Interaction Functions in Collaborative Filtering. - Quanming Yao; Xiangning Chen; James T. Kwok; Yong Li 008 (CoRR, 2019)

Multi-objective Neural Architecture Search via Predictive Network Performance Optimization. - Han Shi; Renjie Pi; Hang Xu 004; Zhenguo Li; James T. Kwok; Tong Zhang 001 (CoRR, 2019)

Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers. - Quanming Yao; James T. Kwok; Taifeng Wang; Tie-Yan Liu (IEEE Trans. Pattern Anal. Mach. Intell., 2019)

Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion. - Quanming Yao; James T. Kwok (IEEE Trans. Knowl. Data Eng., 2019)

Low-Rank Matrix Learning Using Biconvex Surrogate Minimization. - En-Liang Hu; James T. Kwok (IEEE Trans. Neural Networks Learn. Syst., 2019)

Effective Decoding in Graph Auto-Encoder Using Triadic Closure. - Han Shi; Haozheng Fan; James T. Kwok (AAAI, 2020)

Searching to Exploit Memorization Effect in Learning with Noisy Labels. - Quanming Yao; Hansi Yang; Bo Han 003; Gang Niu 001; James Tin-Yau Kwok (ICML, 2020)

Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys. - Rishabh Mehrotra; Ben Carterette; Yong Li 008; Quanming Yao; Chen Gao 001; James T. Kwok; Qiang Yang 001; Isabelle Guyon (KDD, 2020)

Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network. - Lifeng Shen; Zhuocong Li; James T. Kwok (NeurIPS, 2020)

Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. - Han Shi; Renjie Pi; Hang Xu 004; Zhenguo Li; James T. Kwok; Tong Zhang 001 (NeurIPS, 2020)

Efficient Neural Interaction Function Search for Collaborative Filtering. - Quanming Yao; Xiangning Chen; James T. Kwok; Yong Li 008; Cho-Jui Hsieh (WWW, 2020)

Efficient Low-Rank Matrix Learning by Factorizable Nonconvex Regularization. - Yaqing Wang 002; Quanming Yao; James T. Kwok (CoRR, 2020)

A Survey of Label-noise Representation Learning: Past, Present and Future. - Bo Han 003; Quanming Yao; Tongliang Liu; Gang Niu 001; Ivor W. Tsang; James T. Kwok; Masashi Sugiyama (CoRR, 2020)

Generalized Convolutional Sparse Coding With Unknown Noise. - Yaqing Wang 002; James T. Kwok; Lionel M. Ni (IEEE Trans. Image Process., 2020)

Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. - Xiawei Guo; Quanming Yao; James T. Kwok; Wei-Wei Tu; Yuqiang Chen; Wenyuan Dai; Qiang Yang 001 (Federated Learning, 2020)

Time Series Anomaly Detection with Multiresolution Ensemble Decoding. - Lifeng Shen; Zhongzhong Yu; Qianli Ma 001; James T. Kwok (AAAI, 2021)

SparseBERT: Rethinking the Importance Analysis in Self-attention. - Han Shi; Jiahui Gao; Xiaozhe Ren; Hang Xu 004; Xiaodan Liang; Zhenguo Li; James Tin-Yau Kwok (ICML, 2021)

SEEN: Few-Shot Classification with SElf-ENsemble. - Weisen Jiang; Yu Zhang 006; James T. Kwok (IJCNN, 2021)

TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. - Haoang Chi; Feng Liu 003; Wenjing Yang 002; Long Lan; Tongliang Liu; Bo Han 003; William K. Cheung 001; James T. Kwok (NeurIPS, 2021)

Effective Meta-Regularization by Kernelized Proximal Regularization. - Weisen Jiang; James T. Kwok; Yu Zhang 006 (NeurIPS, 2021)

Dropout's Dream Land: Generalization from Learned Simulators to Reality. - Zac Wellmer; James T. Kwok (ECML/PKDD (1), 2021)

A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning. - Yaqing Wang 002; Quanming Yao; James T. Kwok (WWW, 2021)

Tensorizing Subgraph Search in the Supernet. - Hansi Yang; Quanming Yao; James T. Kwok (CoRR, 2021)

Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution. - Huapeng Wu; Jie Gui; Jun Zhang 024; James T. Kwok; Zhihui Wei (CoRR, 2021)

Generalizing from a Few Examples: A Survey on Few-shot Learning. - Yaqing Wang 002; Quanming Yao; James T. Kwok; Lionel M. Ni (ACM Comput. Surv., 2021)

Side Information Fusion for Recommender Systems over Heterogeneous Information Network. - Huan Zhao 002; Quanming Yao; Yangqiu Song; James T. Kwok; Dik Lun Lee (ACM Trans. Knowl. Discov. Data, 2021)

Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval. - Yuan Cao 005; Heng Qi; Jie Gui; Keqiu Li; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Multim., 2021)

Noniterative Sparse LS-SVM Based on Globally Representative Point Selection. - Yuefeng Ma; Xun Liang 001; Gang Sheng; James T. Kwok; Maoli Wang; Guangshun Li (IEEE Trans. Neural Networks Learn. Syst., 2021)

Query Rewriting in TaoBao Search. - Sen Li 001; Fuyu Lv; Taiwei Jin; Guiyang Li; Yukun Zheng; Tao Zhuang; Qingwen Liu 002; Xiaoyi Zeng; James T. Kwok; Qianli Ma 001 (CIKM, 2022)

Revisiting Over-smoothing in BERT from the Perspective of Graph. - Han Shi; Jiahui Gao; Hang Xu 004; Xiaodan Liang; Zhenguo Li; Lingpeng Kong; Stephen M. S. Lee; James T. Kwok (ICLR, 2022)

Subspace Learning for Effective Meta-Learning. - Weisen Jiang; James T. Kwok; Yu Zhang 006 (ICML, 2022)

Efficient Variance Reduction for Meta-learning. - Hansi Yang; James T. Kwok (ICML, 2022)

Pyramidal dense attention networks for single image super-resolution. - Huapeng Wu; Jie Gui; Jun Zhang 024; James T. Kwok; Zhihui Wei (IET Image Process., 2022)

New transformation method in continuous particle swarm optimisation for feature selection. - Kangshun Li; Dunmin Chen; Zhaolian Zeng; Guang Chen; James Tin-Yau Kwok (Int. J. Wirel. Mob. Comput., 2022)

Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. - Quanming Yao; Yaqing Wang 002; Bo Han 003; James T. Kwok (J. Mach. Learn. Res., 2022)

Efficient Low-Rank Semidefinite Programming With Robust Loss Functions. - Quanming Yao; Hansi Yang; En-Liang Hu; James T. Kwok (IEEE Trans. Pattern Anal. Mach. Intell., 2022)

Positive-Unlabeled Node Classification with Structure-aware Graph Learning. - Hansi Yang; Yongqi Zhang; Quanming Yao; James T. Kwok (CIKM, 2023)

Leveraging per Image-Token Consistency for Vision-Language Pre-training. - Yunhao Gou; Tom Ko; Hansi Yang; James T. Kwok; Yu Zhang 006; Mingxuan Wang (CVPR, 2023)

KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. - Yanbin Wei; Qiushi Huang; Yu Zhang 006; James T. Kwok (EMNLP (Findings), 2023)

Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition. - Yuhui Guo; Xun Liang 001; James T. Kwok; Xiangping Zheng; Bo Wu 026; Yuefeng Ma (ICASSP, 2023)

GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training. - Xinchi Deng; Han Shi; Runhui Huang; Changlin Li; Hang Xu 004; Jianhua Han; James T. Kwok; Shen Zhao; Wei Zhang 196; Xiaodan Liang (ICCV, 2023)

An Adaptive Policy to Employ Sharpness-Aware Minimization. - Weisen Jiang; Hansi Yang; Yu Zhang 006; James T. Kwok (ICLR, 2023)

Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts. - Zhili Liu; Kai Chen 023; Jianhua Han; Lanqing Hong; Hang Xu 004; Zhenguo Li; James T. Kwok (ICLR, 2023)

Enhancing Meta Learning via Multi-Objective Soft Improvement Functions. - Runsheng Yu; Weiyu Chen; Xinrun Wang; James T. Kwok (ICLR, 2023)

Effective Structured Prompting by Meta-Learning and Representative Verbalizer. - Weisen Jiang; Yu Zhang 006; James T. Kwok (ICML, 2023)

Non-autoregressive Conditional Diffusion Models for Time Series Prediction. - Lifeng Shen; James T. Kwok (ICML, 2023)

Nonparametric Iterative Machine Teaching. - Chen Zhang; Xiaofeng Cao 002; Weiyang Liu; Ivor W. Tsang; James T. Kwok (ICML, 2023)

Efficient Hyper-parameter Optimization with Cubic Regularization. - Zhenqian Shen; Hansi Yang; Yong Li 008; James T. Kwok; Quanming Yao (NeurIPS, 2023)

Nonparametric Teaching for Multiple Learners. - Chen Zhang; Xiaofeng Cao 002; Weiyang Liu; Ivor W. Tsang; James T. Kwok (NeurIPS, 2023)

Adversarial Attack and Defense for Dehazing Networks. - Jie Gui; Xiaofeng Cong; Chengwei Peng; Yuan Yan Tang; James Tin-Yau Kwok (CoRR, 2023)

No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation. - Biwei Cao; Lulu Hua; Jiuxin Cao; Jie Gui; Bo Liu 004; James Tin-Yau Kwok (CoRR, 2023)

Forward-Backward Reasoning in Large Language Models for Verification. - Weisen Jiang; Han Shi; Longhui Yu; Zhengying Liu; Yu Zhang 006; Zhenguo Li; James T. Kwok (CoRR, 2023)

PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis. - Junsong Chen; Jincheng Yu; Chongjian Ge; Lewei Yao; Enze Xie; Yue Wu 002; Zhongdao Wang; James T. Kwok; Ping Luo 002; Huchuan Lu; Zhenguo Li (CoRR, 2023)

Effective and Parameter-Efficient Reusing Fine-Tuned Models. - Weisen Jiang; Baijiong Lin; Han Shi; Yu Zhang 006; Zhenguo Li; James T. Kwok (CoRR, 2023)

Geom-Erasing: Geometry-Driven Removal of Implicit Concept in Diffusion Models. - Zhili Liu; Kai Chen 023; Yifan Zhang; Jianhua Han; Lanqing Hong; Hang Xu 004; Zhenguo Li; Dit-Yan Yeung; James T. Kwok (CoRR, 2023)

Aggregation Weighting of Federated Learning via Generalization Bound Estimation. - Mingwei Xu; Xiaofeng Cao 002; Ivor W. Tsang; James T. Kwok (CoRR, 2023)

Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning. - Yunhao Gou; Zhili Liu; Kai Chen 023; Lanqing Hong; Hang Xu 004; Aoxue Li; Dit-Yan Yeung; James T. Kwok; Yu Zhang 006 (CoRR, 2023)

Bilinear Scoring Function Search for Knowledge Graph Learning. - Yongqi Zhang; Quanming Yao; James T. Kwok (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning. - Jidong Ge; Yuxiang Liu; Jie Gui; Lanting Fang; Ming Lin 002; James Tin-Yau Kwok; Liguo Huang; Bin Luo 003 (IEEE Trans. Image Process., 2023)

AlignVE: Visual Entailment Recognition Based on Alignment Relations. - Biwei Cao; Jiuxin Cao; Jie Gui; Jiayun Shen; Bo Liu 004; Lei He; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Multim., 2023)

Searching a High Performance Feature Extractor for Text Recognition Network. - Hui Zhang 085; Quanming Yao; James T. Kwok; Xiang Bai (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Feedback Pyramid Attention Networks for Single Image Super-Resolution. - Huapeng Wu; Jie Gui; Jun Zhang 024; James T. Kwok; Zhihui Wei (IEEE Trans. Circuits Syst. Video Technol., 2023)

Forward-Backward Reasoning in Large Language Models for Mathematical Verification. - Weisen Jiang; Han Shi; Longhui Yu; Zhengying Liu; Yu Zhang 006; Zhenguo Li; James T. Kwok (ACL (Findings), 2024)

Eyes Closed, Safety on: Protecting Multimodal LLMs via Image-to-Text Transformation. - Yunhao Gou; Kai Chen 023; Zhili Liu; Lanqing Hong; Hang Xu 004; Zhenguo Li; Dit-Yan Yeung; James T. Kwok; Yu Zhang 006 (ECCV (17), 2024)

Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy. - Tao Li; Weisen Jiang; Fanghui Liu 001; Xiaolin Huang; James T. Kwok (ECCV (65), 2024)

Implicit Concept Removal of Diffusion Models. - Zhili Liu; Kai Chen 023; Yifan Zhang; Jianhua Han; Lanqing Hong; Hang Xu 004; Zhenguo Li; Dit-Yan Yeung; James T. Kwok (ECCV (21), 2024)

PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis. - Junsong Chen; Jincheng Yu; Chongjian Ge; Lewei Yao; Enze Xie; Zhongdao Wang; James T. Kwok; Ping Luo 002; Huchuan Lu; Zhenguo Li (ICLR, 2024)

Multi-Resolution Diffusion Models for Time Series Forecasting. - Lifeng Shen; Weiyu Chen; James T. Kwok (ICLR, 2024)

MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. - Longhui Yu; Weisen Jiang; Han Shi; Jincheng Yu; Zhengying Liu; Yu Zhang 006; James T. Kwok; Zhenguo Li; Adrian Weller; Weiyang Liu (ICLR, 2024)

Efficient Pareto Manifold Learning with Low-Rank Structure. - Weiyu Chen; James T. Kwok (ICML, 2024)

Improving Sharpness-Aware Minimization by Lookahead. - Runsheng Yu; Youzhi Zhang 001; James T. Kwok (ICML, 2024)

RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models. - Shuhao Chen; Weisen Jiang; Baijiong Lin; James T. Kwok; Yu Zhang 006 (NeurIPS, 2024)

GITA: Graph to Visual and Textual Integration for Vision-Language Graph Reasoning. - Yanbin Wei; Shuai Fu; Weisen Jiang; Zejian Zhang; Zhixiong Zeng; Qi Wu; James T. Kwok; Yu Zhang 006 (NeurIPS, 2024)

Rendering Graphs for Graph Reasoning in Multimodal Large Language Models. - Yanbin Wei; Shuai Fu; Weisen Jiang; James T. Kwok; Yu Zhang 006 (CoRR, 2024)

Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment. - Zhili Liu; Yunhao Gou; Kai Chen 023; Lanqing Hong; Jiahui Gao; Fei Mi; Yu Zhang 006; Zhenguo Li; Xin Jiang 002; Qun Liu 001; James T. Kwok (CoRR, 2024)

Direct Alignment of Language Models via Quality-Aware Self-Refinement. - Runsheng Yu; Yong Wang; Xiaoqi Jiao; Youzhi Zhang 001; James T. Kwok (CoRR, 2024)

Mixup Augmentation with Multiple Interpolations. - Lifeng Shen; Jincheng Yu; Hansi Yang; James T. Kwok (CoRR, 2024)

Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning. - Hansi Yang; James T. Kwok (CoRR, 2024)

Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models. - Siyu Zhai; Zhibo He; Xiaofeng Cong; Junming Hou; Jie Gui; Jian Wei You; Xin Gong; James Tin-Yau Kwok; Yuan Yan Tang (CoRR, 2024)

EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions. - Kai Chen; Yunhao Gou; Runhui Huang; Zhili Liu; Daxin Tan; Jing Xu; Chunwei Wang; Yi Zhu; Yihan Zeng; Kuo Yang; Dingdong Wang; Kun Xiang; et al. (CoRR, 2024)

Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation. - Xiaofeng Cong; Jing Zhang 037; Yeying Jin; Junming Hou; Yu Zhao; Jie Gui; James Tin-Yau Kwok; Yuan Yan Tang (CoRR, 2024)

Fooling the Image Dehazing Models by First Order Gradient. - Jie Gui; Xiaofeng Cong; Chengwei Peng; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Circuits Syst. Video Technol., 2024)

CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition. - Yifan Wang; Jie Gui; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Inf. Forensics Secur., 2024)

Constructing Diverse Inlier Consistency for Partial Point Cloud Registration. - Yu-Xin Zhang 004; Jie Gui; James Tin-Yau Kwok (IEEE Trans. Image Process., 2024)

Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding. - Jie Gui; Xiaofeng Cong; Lei He; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Multim., 2024)

Power Law in Deep Neural Networks: Sparse Network Generation and Continual Learning With Preferential Attachment. - Fan Feng; Lu Hou 002; Qi She; Rosa H. M. Chan; James T. Kwok (IEEE Trans. Neural Networks Learn. Syst., 2024)

Response Generation in Social Network With Topic and Emotion Constraints. - Biwei Cao; Jiuxin Cao; Bo Liu 004; Jie Gui; Jun Zhou 027; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Comput. Soc. Syst., 2024)

Automated Dominative Subspace Mining for Efficient Neural Architecture Search. - Yaofo Chen; Yong Guo; Daihai Liao; Fanbing Lv; Hengjie Song; James Tin-Yau Kwok; Mingkui Tan (IEEE Trans. Circuits Syst. Video Technol., 2024)

Searching to Exploit Memorization Effect in Deep Learning With Noisy Labels. - Hansi Yang; Quanming Yao; Bo Han 003; James T. Kwok (IEEE Trans. Pattern Anal. Mach. Intell., 2024)

A Survey on Time-Series Pre-Trained Models. - Qianli Ma 001; Zhen Liu 023; Zhenjing Zheng; Ziyang Huang; Siying Zhu; Zhongzhong Yu; James T. Kwok (IEEE Trans. Knowl. Data Eng., 2024)

Mixture of insighTful Experts (MoTE): The Synergy of Reasoning Chains and Expert Mixtures in Self-Alignment. - Zhili Liu; Yunhao Gou; Kai Chen 023; Lanqing Hong; Jiahui Gao; Fei Mi; Yu Zhang 006; Zhenguo Li; Xin Jiang 002; Qun Liu 001; James T. Kwok (ACL (1), 2025)

EMOVA: Empowering Language Models to See, Hear and Speak with Vivid Emotions. - Kai Chen; Yunhao Gou; Runhui Huang; Zhili Liu; Daxin Tan; Jing Xu; Chunwei Wang; Yi Zhu; Yihan Zeng; Kuo Yang; Dingdong Wang; Kun Xiang; et al. (CVPR, 2025)

Joint Gradient Balancing for Data Ordering in Finite-Sum Multi-Objective Optimization. - Hansi Yang; James T. Kwok (ICLR, 2025)

Domain-guided conditional diffusion model for unsupervised domain adaptation. - Yulong Zhang; Shuhao Chen; Weisen Jiang; Yu Zhang 006; Jiangang Lu; James T. Kwok (Neural Networks, 2025)

Improving Fast Adversarial Training via Self-Knowledge Guidance. - Chengze Jiang; Junkai Wang; Minjing Dong; Jie Gui; Xinli Shi; Yuan Cao 005; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Inf. Forensics Secur., 2025)

ColorVein: Colorful Cancelable Vein Biometrics. - Yifan Wang; Jie Gui; Xinli Shi; Linqing Gui; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Inf. Forensics Secur., 2025)

Divide and Conquer: Frequency-Aware Contrastive Adversarial Training for Robust Point Cloud Classification. - Yu-Xin Zhang 004; Jie Gui; Minjing Dong; Xiaofeng Cong; Yuan Cao 005; Xin Gong; Yuan Yan Tang; James Tin-Yau Kwok (IEEE Trans. Inf. Forensics Secur., 2025)

Gradient-Based Multi-Objective Deep Learning: Algorithms, Theories, Applications, and Beyond. - Weiyu Chen; Xiaoyuan Zhang; Baijiong Lin; Xi Lin 001; Han Zhao 002; Qingfu Zhang 001; James T. Kwok (CoRR, 2025)

Corrupted but Not Broken: Rethinking the Impact of Corrupted Data in Visual Instruction Tuning. - Yunhao Gou; Hansi Yang; Zhili Liu; Kai Chen 023; Yihan Zeng; Lanqing Hong; Zhenguo Li; Qun Liu 001; James T. Kwok; Yu Zhang 006 (CoRR, 2025)

Sandbox: safeguarded multi-label learning through safe optimal transport. - Lefei Zhang; Geng Yu; Jiangchao Yao; Yew-Soon Ong; Ivor W. Tsang; James T. Kwok (Mach. Learn., 2025)

A Survey on Small Sample Imbalance Problem: Metrics, Feature Analysis, and Solutions. - Shuxian Zhao; Jie Gui; Minjing Dong; Baosheng Yu; Zhipeng Gui; Lu Dong; Yuan Yan Tang; James Tin-Yau Kwok (CoRR, 2025)

Open the Eyes of MPNN: Vision Enhances MPNN in Link Prediction. - Yanbin Wei; Xuehao Wang; Zhan Zhuang; Yang Chen; Shuhao Chen; Yulong Zhang; Yu Zhang 006; James T. Kwok (CoRR, 2025)

Multi-Order Wavelet Derivative Transform for Deep Time Series Forecasting. - Ziyu Zhou; Jiaxi Hu; Qingsong Wen; James T. Kwok; Yuxuan Liang (CoRR, 2025)

Perceptual Decoupling for Scalable Multi-modal Reasoning via Reward-Optimized Captioning. - Yunhao Gou; Kai Chen; Zhili Liu; Lanqing Hong; Xin Jin 005; Zhenguo Li; James T. Kwok; Yu Zhang (CoRR, 2025)

DynamicMind: A Tri-Mode Thinking System for Large Language Models. - Wei Li; Yanbin Wei; Qiushi Huang; Jiangyue Yan; Yang Chen; James T. Kwok; Yu Zhang 006 (CoRR, 2025)

Revitalizing Canonical Pre-Alignment for Irregular Multivariate Time Series Forecasting. - Ziyu Zhou; Yiming Huang; Yanyun Wang 003; Yuankai Wu; James T. Kwok; Yuxuan Liang (CoRR, 2025)

Harnessing Adaptive Topology Representations for Zero-Shot Graph Question Answering. - Yanbin Wei; Jiangyue Yan; Chun Kang; Yang Chen; Hua Liu; James T. Kwok; Yu Zhang 006 (CoRR, 2025)