PhD in Computer Science
The Hong Kong University of Science and Technology, 1996
Automated Dominative Subspace Mining for Efficient Neural Architecture Search
Article
CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition
Article
Fooling the Image Dehazing Models by First Order Gradient
Article
Response Generation in Social Network With Topic and Emotion Constraints
Article
Searching to Exploit Memorization Effect in Deep Learning with Noisy Labels
Article
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation
Conference paper
Forward-Backward Reasoning in Large Language Models for Mathematical Verification
Conference paper
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy
Conference paper
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Conference paper
Multi-resolution diffusion models for time series forecasting
Conference paper
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-To-Image Synthesis
Conference paper
Bilinear Scoring Function Search for Knowledge Graph Learning
Article
Feedback Pyramid Attention Networks for Single Image Super-Resolution
Article
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding
Article
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Article
Searching a High Performance Feature Extractor for Text Recognition Network
Article
An adaptive policy to employ sharpness-aware minimization
Conference paper
Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition
Conference paper
Effective structured-prompting by meta-learning and representative verbalizer
Conference paper
Efficient hyper-parameter optimization with cubic regularization
Conference paper
Efficient Hyper-parameter Optimization with Cubic Regularization
Conference paper
Enhancing meta learning via multi-objective soft improvement functions
Conference paper
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training
Conference paper
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
Conference paper
Leveraging per Image-Token Consistency for Vision-Language Pre-training
Conference paper
Non-autoregressive conditional diffusion models for time series prediction.
Conference paper
Nonparametric iterative machine teaching
Conference paper
Nonparametric Teaching for Multiple Learners
Conference paper
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
Conference paper
Task-customized masked autoencoder via mixture of cluster-conditional experts
Conference paper
AlignVE: Visual Entailment Recognition Based on Alignment Relations
Article
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
Article
Efficient low-rank tensor learning with nonconvex overlapped nuclear norm regularization
Article
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
Article
New transformation method in continuous particle swarm optimisation for feature selection
Article
Article
Pyramidal dense attention networks for single image super-resolution
Article
Efficient Variance Reduction for Meta-Learning
Conference paper
Multi-objective deep learning with adaptive reference vectors
Conference paper
Query Rewriting in TaoBao Search
Conference paper
Revisiting over-smoothing in BERT from the perspective of graph
Conference paper
Subspace Learning for Effective Meta-Learning
Conference paper
Noniterative Sparse LS-SVM Based on Globally Representative Point Selection
Article
Side Information Fusion for Recommender Systems over Heterogeneous Information Network
Article
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning
Conference paper
Dropout's Dream Land: Generalization from Learned Simulators to Reality
Conference paper
Effective Meta-Regularization by Kernelized Proximal Regularization
Conference paper
SEEN: Few-Shot Classification with SElf-ENsemble
Conference paper
SparseBERT: Rethinking the Importance Analysis in Self-attention
Conference paper
Time series anomaly detection with multiresolution ensemble decoding
Conference paper
TOHAN: A one-step approach towards few-shot hypothesis adaptation
Conference paper
Generalized Convolutional Sparse Coding with Unknown Noise
Article
Generalizing from a few examples: A survey on few-shot learning
Article
Learning to Hash with Dimension Analysis based Quantizer for Image Retrieval
Article
Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction
Book chapter
Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys
Conference paper
Bridging the gap between sample-based and one-shot neural architecture search with BONAS
Conference paper
Effective Decoding in Graph Auto-Encoder Using Triadic Closure
Conference paper
Efficient Neural Interaction Function Search for Collaborative Filtering
Conference paper
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Conference paper
Timeseries anomaly detection using temporal hierarchical one-class network
Conference paper
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion
Article
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Article
Low-rank Matrix Learning Using Biconvex Surrogate Minimization
Article
Conference paper
Communication-efficient distributed blockwise momentum sgd with error-feedback
Conference paper
Dynamic unit surgery for deep neural network compression and acceleration
Conference paper
Efficient nonconvex regularized tensor completion with structure-aware proximal iterations
Conference paper
Efficient Nonconvex Regularized Tensor Completion with Structure-Aware Proximal Iterations
Conference paper
Normalization helps training of quantized LSTM
Conference paper
Conference paper
Privacy-preserving stacking with application to cross-organizational diabetes prediction
Conference paper
Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity
Article
Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set
Article
Multi-Label Learning with Global and Local Label Correlation
Article
Scalable Online Convolutional Sparse Coding
Article
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Conference paper
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Conference paper
Loss-aware Weight Quantization of Deep Networks
Conference paper
Online Convolutional Sparse Coding with Sample-dependent Dictionary
Conference paper
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Conference paper
Scalable Robust Matrix Factorization with Nonconvex Loss
Conference paper
A Note on the Unification of Adaptive Online Learning
Article
Multi-Label learning in the independent label sub-spaces
Article
Collaborative Filtering with Social Local Models
Conference paper
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
Conference paper
Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm
Conference paper
Follow the Moving Leader in Deep Learning
Conference paper
Follow the Moving Leader in Deep Learning
Conference paper
Loss-aware Binarization of Deep Networks
Conference paper
Conference paper
Zero-shot Learning With a Partial Set of Observed Attributes
Conference paper
Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering
Conference paper
Asynchronous Distributed Semi-stochastic Gradient Optimization
Conference paper
Efficient Learning of Timeseries Shapelets
Conference paper
Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity
Conference paper
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Conference paper
Fast Nonsmooth Regularized Risk Minimization with Continuation
Conference paper
Fast-and-Light Stochastic ADMM
Conference paper
Greedy Learning of Generalized Low-Rank Models
Conference paper
Towards Safe Semi-supervised Learning for Multivariate Performance Measures
Conference paper
Bayes-Optimal Hierarchical Multilabel Classification
Article
Large-Scale Nystrom Kernel Matrix Approximation Using Randomized SVD
Article
Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent
Article
Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines
Article
Book chapter
Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion
Conference paper
Collaborative Filtering via Co-Factorization of Individuals and Groups
Conference paper
Colorization by patch-based local low-rank matrix completion
Conference paper
Fast Low-Rank Matrix Learning with Nonconvex Regularization
Conference paper
Fast Second-order Stochastic Backpropagation for Variational Inference
Conference paper
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification
Article
Article
Simple randomized algorithms for online learning with kernels
Article
Accelerated stochastic gradient method for composite regularization
Conference paper
Accurate Integration of Aerosol Predictions by Smoothing on a Manifold
Conference paper
Asynchronous Distributed ADMM for Consensus Optimization
Conference paper
Asynchronous Distributed ADMM for Consensus Optimization
Conference paper
Fast stochastic alternating direction method of multipliers
Conference paper
Fast Stochastic Alternating Direction Method of Multipliers
Conference paper
Gradient Descent with Proximal Average for Nonconvex and Composite Regularization
Conference paper
Learning to predict from crowd sourced data
Conference paper
Learning to Predict from Crowdsourced Data
Conference paper
Multilabel Classification With Label Correlations and Missing Labels
Conference paper
Convex and Scalable Weakly Labeled SVMs
Article
Accurate Probability Calibration for Multiple Classifiers
Conference paper
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels
Conference paper
Efficient Kernel Learning from Side Information Using ADMM
Conference paper
Efficient Learning for Models with DAG-Structured Parameter Constraints
Conference paper
Efficient Multi-label Classification with Many Labels
Conference paper
Flexible Nonparametric Kernel Learning with Different Loss Functions
Conference paper
Learning from High-Dimensional Data in Multitask/Multilabel Classification
Conference paper
A brief introduction to the special issue for ISNN2010
Article
Bilinear Probabilistic Principal Component Analysis
Article
Efficient Sparse Modeling With Automatic Feature Grouping
Article
Convex multitask learning with flexible task clusters
Conference paper
Hierarchical Multilabel Classification with Minimum Bayes Risk
Conference paper
Mandatory leaf node prediction in hierarchical multilabel classification
Conference paper
A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions
Article
Domain Adaptation via Transfer Component Analysis
Article
Incorporating cellular sorting structure for better prediction of protein subcellular locations
Article
Neural Information Processing - 18th International Conference, Proceedings
Book
Efficient sparse modeling with automatic feature grouping
Conference paper
Multi-label classification on tree- and DAG-structured hierarchies
Conference paper
Structured clustering with automatic kernel adaptation
Conference paper
Time and Space Efficient Spectral Clustering via Column Sampling
Conference paper
Clustered Nyström method for large scale manifold learning and dimension reduction
Article
Fast and accurate kernel density approximation using a divide-and-conquer approach
Article
Incorporating the loss function into discriminative clustering of structured outputs
Article
Article
Article
Simplifying Mixture Models Through Function Approximation
Article
Text detection in images using sparse representation with discriminative dictionaries
Article
Book
Cost-sensitive semi-supervised support vector machine
Conference paper
Making large-scale Nyström approximation possible
Conference paper
Manifold Regularization for Structured Outputs via the Joint Kernel
Conference paper
Online Multiple Instance Learning with No Regret
Conference paper
Spectral and semidefinite relaxations of the CLUHSIC algorithm
Conference paper
Building Sparse Multiple-Kernel SVM Classifiers
Article
Density-Weighted Nystrom Method for Computing Large Kernel Eigensystems
Article
Maximum Margin Clustering Made Practical
Article
Maximum Penalized Likelihood Kernel Regression for Fast Adaptation
Article
Article
A convex method for locating regions of interest with multi-instance learning
Conference paper
Accelerated gradient method for multi-task sparse learning problem
Conference paper
Accelerated gradient methods for stochastic optimization and online learning
Conference paper
Domain Adaptation via Transfer Component Analysis
Conference paper
Maximum margin clustering with multivariate loss function
Conference paper
Conference paper
Prototype vector machine for large scale semi-supervised learning
Conference paper
Semi-Supervised learning using label mean
Conference paper
Tighter and convex maximum margin clustering
Conference paper
Unsupervised maximum margin feature selection with manifold regularization
Conference paper
Large-scale maximum margin discriminant analysis using core vector machines
Article
Matrix-Variate Factor Analysis and Its Applications
Article
Sliced coordinate analysis for effective dimension reduction and nonlinear extensions
Article
Improved Nyström low-rank approximation and error analysis
Conference paper
Transfer learning via dimensionality reduction
Conference paper
Transferring localization models across space
Conference paper
A class of single-class minimax probability machines for novelty detection
Article
Article
Face recognition using spectral features
Article
Surrogate Maximization/Minimization Algorithms and Extensions
Article
Article
Adaptive localization in a dynamic WiFi environment through multi-view learning
Conference paper
Ensembles of Partially Trained SVMs with Multiplicative Updates
Conference paper
Large-scale sparsified manifold regularization
Conference paper
Marginalized Multi-Instance Kernels
Conference paper
Maximum margin clustering made practical
Conference paper
Simpler core vector machines with enclosing balls
Conference paper
Surrogate maximization/minimization algorithms and extensions
Conference paper
A novel incremental principal component analysis and its application for face recognition
Article
Accelerated convergence using dynamic mean shift
Article
Diversified SVM ensembles for large data sets
Article
Efficient hyperkernel learning using second-order cone programming
Article
Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting
Article
Facial image reconstruction by SVDD-based pattern de-noising
Article
Gene feature extraction using T-test statistics and kernel partial least squares
Article
Generalized core vector machines
Article
Model-based transductive learning of the kernel matrix
Article
Article
Book
A regularization framework for multiple-instance learning
Conference paper
Block-quantized kernel matrix for fast spectral embedding
Conference paper
Efficient classification of multi-label and imbalanced data using min-max modular classifiers
Conference paper
Efficient kernel feature extraction for massive data sets
Conference paper
Fast speaker adaption via maximum penalized likelihood kernel regression
Conference paper
Learning the kernel in mahalanobis one-class support vector machines
Conference paper
Locally adaptive classification piloted by uncertainty
Conference paper
Multimodal registration using the discrete wavelet frame transform
Conference paper
Simplifying mixture models through function approximation
Conference paper
Wavelet-based feature extraction for microarray data classification
Conference paper
Core vector machines: Fast SVM training on very large data sets
Article
Kernel eigenvoice speaker adaptation
Article
Accurate and Low-cost Location Estimation Using Kernels
Conference paper
Applying neighborhood consistency for fast clustering and kernel density estimation
Conference paper
Core vector regression for very large regression problems
Conference paper
Data-dependent kernels for high-dimensional data classification
Conference paper
Kernel relevant component analysis for distance metric learning
Conference paper
Page segmentation using mathematical morphology
Conference paper
Pattern de-noising based on support ector data description
Conference paper
Position estimation tor wireless sensor networks
Conference paper
Conference paper
Very large SVM training using core vector machines
Conference paper
Dissimilarity learning for nominal data
Article
Efficient hyperkernel learning using second-order cone programming
Article
Fusing images with different focuses using support vector machines
Article
Signal de-noising by improving soft thresholding on the dyadic wavelet transform
Article
The pre-image problem in kernel methods
Article
Conference paper
Bayesian inference on principal component analysis using reversible jump markov chain Monte Carlo
Conference paper
Eigenvoice speaker adaptation via composite kernel PCA
Conference paper
Incremental PCA based face recognition
Conference paper
Scaling up support vector data description by using core-sets
Conference paper
Speedup of Kernel Eigenvoice Speaker Adaptation by Embedded Kernel PCA
Conference paper
Study of various composite kernels for kernel eigenvoice speaker adaptation
Conference paper
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
Conference paper
Text extraction using edge detection and morphological dilation
Conference paper
Using kernel PCA to improve eigenvoice speaker adaptation
Conference paper
Linear dependency between ε and the input noise in ε-support vector regression
Article
Mining customer product rating for personalized marketing
Article
Texture classification using the support vector machines
Article
Distance metric learning with kernels
Conference paper
Incremental eigen decomposition
Conference paper
Parametric distance metric learning with label information
Conference paper
The pre-image problem in kernel methods
Conference paper
Fusing images with multiple focuses using support vector machines
Article
Multifocus image fusion using artificial neural networks
Article
Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images
Article
Finding the pre-images in kernel principal component analysis
Conference paper
Fusing images with multiple focuses using support vector machines
Conference paper
Improving de-noising by coefficient de-noising and dyadic wavelet transform
Conference paper
Combination of images with diverse focuses using the spatial frequency
Article
Linear dependency between epsilon and the input noise in epsilon-support vector regression
Article
Applying the Bayesian evidence framework to nu-support vector regression
Conference paper
Bayesian support vector regression
Conference paper
The evidence framework applied to support vector machines
Article
An extended genetic rule induction algorithm
Conference paper
Conference paper
Rival penalized competitive learning for model-based sequence clustering
Conference paper
Moderating the outputs of support vector machine classifiers
Article
Integrating the evidence framework and the support vector machine
Conference paper
Moderating the outputs of support vector machine classifiers
Conference paper
Automated text categorization using support vector machine
Conference paper
Support vector mixture for classification and regression problems
Conference paper
Article
Objective functions for training new hidden units in constructive neural networks
Article
Use of bias term in projection pursuit learning improves approximation and convergence properties
Article
Bayesian regularization in constructive neural networks
Conference paper
Reference priors for neural networks: Laplace versus Gaussian
Conference paper
IMPROVING THE APPROXIMATION AND CONVERGENCE CAPABILITIES OF PROJECTION PURSUIT LEARNING
Article
Efficient Cross-Validation for Feedforward Neural Networks
Conference paper
A theoretically sound learning algorithm for constructive neural networks
Conference paper
Constructive Neural Networks : Some Practical Considerations
Conference paper
Experimental Analysis Of Input Weight Freezing In Constructive Neural Networks
Conference paper
Automated Dominative Subspace Mining for Efficient Neural Architecture Search
CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition
Response Generation in Social Network With Topic and Emotion Constraints
Searching to Exploit Memorization Effect in Deep Learning with Noisy Labels
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation
Forward-Backward Reasoning in Large Language Models for Mathematical Verification
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models
Multi-resolution diffusion models for time series forecasting
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-To-Image Synthesis
Bilinear Scoring Function Search for Knowledge Graph Learning
Feedback Pyramid Attention Networks for Single Image Super-Resolution
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning
Searching a High Performance Feature Extractor for Text Recognition Network
Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition
Effective structured-prompting by meta-learning and representative verbalizer
Efficient hyper-parameter optimization with cubic regularization
Efficient Hyper-parameter Optimization with Cubic Regularization
Enhancing meta learning via multi-objective soft improvement functions
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion
Leveraging per Image-Token Consistency for Vision-Language Pre-training
Non-autoregressive conditional diffusion models for time series prediction.
Positive-Unlabeled Node Classification with Structure-aware Graph Learning
Task-customized masked autoencoder via mixture of cluster-conditional experts
AlignVE: Visual Entailment Recognition Based on Alignment Relations
Efficient Low-Rank Semidefinite Programming with Robust Loss Functions
Efficient low-rank tensor learning with nonconvex overlapped nuclear norm regularization
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization
New transformation method in continuous particle swarm optimisation for feature selection
Pyramidal dense attention networks for single image super-resolution
Noniterative Sparse LS-SVM Based on Globally Representative Point Selection
Side Information Fusion for Recommender Systems over Heterogeneous Information Network
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning
Dropout's Dream Land: Generalization from Learned Simulators to Reality
Effective Meta-Regularization by Kernelized Proximal Regularization
SparseBERT: Rethinking the Importance Analysis in Self-attention
Time series anomaly detection with multiresolution ensemble decoding
TOHAN: A one-step approach towards few-shot hypothesis adaptation
Generalizing from a few examples: A survey on few-shot learning
Learning to Hash with Dimension Analysis based Quantizer for Image Retrieval
Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction
Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys
Bridging the gap between sample-based and one-shot neural architecture search with BONAS
Effective Decoding in Graph Auto-Encoder Using Triadic Closure
Efficient Neural Interaction Function Search for Collaborative Filtering
Searching to Exploit Memorization Effect in Learning with Noisy Labels
Timeseries anomaly detection using temporal hierarchical one-class network
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers
Low-rank Matrix Learning Using Biconvex Surrogate Minimization
Communication-efficient distributed blockwise momentum sgd with error-feedback
Dynamic unit surgery for deep neural network compression and acceleration
Efficient nonconvex regularized tensor completion with structure-aware proximal iterations
Efficient Nonconvex Regularized Tensor Completion with Structure-Aware Proximal Iterations
Privacy-preserving stacking with application to cross-organizational diabetes prediction
Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity
Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set
Multi-Label Learning with Global and Local Label Correlation
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Online Convolutional Sparse Coding with Sample-dependent Dictionary
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering
Asynchronous Distributed Semi-stochastic Gradient Optimization
Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Fast Nonsmooth Regularized Risk Minimization with Continuation
Towards Safe Semi-supervised Learning for Multivariate Performance Measures
Large-Scale Nystrom Kernel Matrix Approximation Using Randomized SVD
Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent
Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification
Simple randomized algorithms for online learning with kernels
Accelerated stochastic gradient method for composite regularization
Accurate Integration of Aerosol Predictions by Smoothing on a Manifold
Gradient Descent with Proximal Average for Nonconvex and Composite Regularization
Multilabel Classification With Label Correlations and Missing Labels
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels
Efficient Learning for Models with DAG-Structured Parameter Constraints
Flexible Nonparametric Kernel Learning with Different Loss Functions
Learning from High-Dimensional Data in Multitask/Multilabel Classification
Clustered Nyström method for large scale manifold learning and dimension reduction
Fast and accurate kernel density approximation using a divide-and-conquer approach
Incorporating the loss function into discriminative clustering of structured outputs
Text detection in images using sparse representation with discriminative dictionaries
Density-Weighted Nystrom Method for Computing Large Kernel Eigensystems
Maximum Penalized Likelihood Kernel Regression for Fast Adaptation
A convex method for locating regions of interest with multi-instance learning
Accelerated gradient method for multi-task sparse learning problem
Accelerated gradient methods for stochastic optimization and online learning
Prototype vector machine for large scale semi-supervised learning
Unsupervised maximum margin feature selection with manifold regularization
A class of single-class minimax probability machines for novelty detection
Surrogate Maximization/Minimization Algorithms and Extensions
A novel incremental principal component analysis and its application for face recognition
Efficient hyperkernel learning using second-order cone programming
Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting
Facial image reconstruction by SVDD-based pattern de-noising
Gene feature extraction using T-test statistics and kernel partial least squares
Efficient classification of multi-label and imbalanced data using min-max modular classifiers
Fast speaker adaption via maximum penalized likelihood kernel regression
Learning the kernel in mahalanobis one-class support vector machines
Multimodal registration using the discrete wavelet frame transform
Wavelet-based feature extraction for microarray data classification
Efficient hyperkernel learning using second-order cone programming
Fusing images with different focuses using support vector machines
Signal de-noising by improving soft thresholding on the dyadic wavelet transform
Bayesian inference on principal component analysis using reversible jump markov chain Monte Carlo
Scaling up support vector data description by using core-sets
Speedup of Kernel Eigenvoice Speaker Adaptation by Embedded Kernel PCA
Study of various composite kernels for kernel eigenvoice speaker adaptation
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
Text extraction using edge detection and morphological dilation
Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity
Article
Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set
Article
Multi-Label Learning with Global and Local Label Correlation
Article
Scalable Online Convolutional Sparse Coding
Article
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Conference paper
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data
Conference paper
Loss-aware Weight Quantization of Deep Networks
Conference paper
Online Convolutional Sparse Coding with Sample-dependent Dictionary
Conference paper
Online Convolutional Sparse Coding with Sample-Dependent Dictionary
Conference paper
Scalable Robust Matrix Factorization with Nonconvex Loss
Conference paper
A Note on the Unification of Adaptive Online Learning
Article
Multi-Label learning in the independent label sub-spaces
Article
Collaborative Filtering with Social Local Models
Conference paper
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems
Conference paper
Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm
Conference paper
Follow the Moving Leader in Deep Learning
Conference paper
Follow the Moving Leader in Deep Learning
Conference paper
Loss-aware Binarization of Deep Networks
Conference paper
Conference paper
Zero-shot Learning With a Partial Set of Observed Attributes
Conference paper
Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering
Conference paper
Asynchronous Distributed Semi-stochastic Gradient Optimization
Conference paper
Efficient Learning of Timeseries Shapelets
Conference paper
Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity
Conference paper
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity
Conference paper
Fast Nonsmooth Regularized Risk Minimization with Continuation
Conference paper
Fast-and-Light Stochastic ADMM
Conference paper
Greedy Learning of Generalized Low-Rank Models
Conference paper
Towards Safe Semi-supervised Learning for Multivariate Performance Measures
Conference paper
Bayes-Optimal Hierarchical Multilabel Classification
Article
Large-Scale Nystrom Kernel Matrix Approximation Using Randomized SVD
Article
Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent
Article
Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines
Article
Book chapter
Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion
Conference paper
Collaborative Filtering via Co-Factorization of Individuals and Groups
Conference paper
Colorization by patch-based local low-rank matrix completion
Conference paper
Fast Low-Rank Matrix Learning with Nonconvex Regularization
Conference paper
Fast Second-order Stochastic Backpropagation for Variational Inference
Conference paper
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification
Article
Article
Simple randomized algorithms for online learning with kernels
Article
Accelerated stochastic gradient method for composite regularization
Conference paper
Accurate Integration of Aerosol Predictions by Smoothing on a Manifold
Conference paper
Asynchronous Distributed ADMM for Consensus Optimization
Conference paper
Asynchronous Distributed ADMM for Consensus Optimization
Conference paper
Fast stochastic alternating direction method of multipliers
Conference paper
Fast Stochastic Alternating Direction Method of Multipliers
Conference paper
Gradient Descent with Proximal Average for Nonconvex and Composite Regularization
Conference paper
Learning to predict from crowd sourced data
Conference paper
Learning to Predict from Crowdsourced Data
Conference paper
Multilabel Classification With Label Correlations and Missing Labels
Conference paper
Convex and Scalable Weakly Labeled SVMs
Article
Accurate Probability Calibration for Multiple Classifiers
Conference paper
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels
Conference paper
Efficient Kernel Learning from Side Information Using ADMM
Conference paper
Efficient Learning for Models with DAG-Structured Parameter Constraints
Conference paper
Efficient Multi-label Classification with Many Labels
Conference paper
Flexible Nonparametric Kernel Learning with Different Loss Functions
Conference paper
Learning from High-Dimensional Data in Multitask/Multilabel Classification
Conference paper
A brief introduction to the special issue for ISNN2010
Article
Bilinear Probabilistic Principal Component Analysis
Article
Efficient Sparse Modeling With Automatic Feature Grouping
Article
Convex multitask learning with flexible task clusters
Conference paper
Hierarchical Multilabel Classification with Minimum Bayes Risk
Conference paper
Mandatory leaf node prediction in hierarchical multilabel classification
Conference paper
A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions
Article
Domain Adaptation via Transfer Component Analysis
Article
Incorporating cellular sorting structure for better prediction of protein subcellular locations
Article
Neural Information Processing - 18th International Conference, Proceedings
Book
Efficient sparse modeling with automatic feature grouping
Conference paper
Multi-label classification on tree- and DAG-structured hierarchies
Conference paper
Structured clustering with automatic kernel adaptation
Conference paper
Time and Space Efficient Spectral Clustering via Column Sampling
Conference paper
Clustered Nyström method for large scale manifold learning and dimension reduction
Article
Fast and accurate kernel density approximation using a divide-and-conquer approach
Article
Incorporating the loss function into discriminative clustering of structured outputs
Article
Article
Article
Simplifying Mixture Models Through Function Approximation
Article
Text detection in images using sparse representation with discriminative dictionaries
Article
Book
Cost-sensitive semi-supervised support vector machine
Conference paper
Making large-scale Nyström approximation possible
Conference paper
Manifold Regularization for Structured Outputs via the Joint Kernel
Conference paper
Online Multiple Instance Learning with No Regret
Conference paper
Spectral and semidefinite relaxations of the CLUHSIC algorithm
Conference paper
Building Sparse Multiple-Kernel SVM Classifiers
Article
Density-Weighted Nystrom Method for Computing Large Kernel Eigensystems
Article
Maximum Margin Clustering Made Practical
Article
Maximum Penalized Likelihood Kernel Regression for Fast Adaptation
Article
Article
A convex method for locating regions of interest with multi-instance learning
Conference paper
Accelerated gradient method for multi-task sparse learning problem
Conference paper
Accelerated gradient methods for stochastic optimization and online learning
Conference paper
Domain Adaptation via Transfer Component Analysis
Conference paper
Maximum margin clustering with multivariate loss function
Conference paper
Conference paper
Prototype vector machine for large scale semi-supervised learning
Conference paper
Semi-Supervised learning using label mean
Conference paper
Tighter and convex maximum margin clustering
Conference paper
Unsupervised maximum margin feature selection with manifold regularization
Conference paper
Large-scale maximum margin discriminant analysis using core vector machines
Article
Matrix-Variate Factor Analysis and Its Applications
Article
Sliced coordinate analysis for effective dimension reduction and nonlinear extensions
Article
Improved Nyström low-rank approximation and error analysis
Conference paper
Transfer learning via dimensionality reduction
Conference paper
Transferring localization models across space
Conference paper
A class of single-class minimax probability machines for novelty detection
Article
Article
Face recognition using spectral features
Article
Surrogate Maximization/Minimization Algorithms and Extensions
Article
Article
Adaptive localization in a dynamic WiFi environment through multi-view learning
Conference paper
Ensembles of Partially Trained SVMs with Multiplicative Updates
Conference paper
Large-scale sparsified manifold regularization
Conference paper
Marginalized Multi-Instance Kernels
Conference paper
Maximum margin clustering made practical
Conference paper
Simpler core vector machines with enclosing balls
Conference paper
Surrogate maximization/minimization algorithms and extensions
Conference paper
A novel incremental principal component analysis and its application for face recognition
Article
Accelerated convergence using dynamic mean shift
Article
Diversified SVM ensembles for large data sets
Article
Efficient hyperkernel learning using second-order cone programming
Article
Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting
Article
Facial image reconstruction by SVDD-based pattern de-noising
Article
Gene feature extraction using T-test statistics and kernel partial least squares
Article
Generalized core vector machines
Article
Model-based transductive learning of the kernel matrix
Article
Article
Book
A regularization framework for multiple-instance learning
Conference paper
Block-quantized kernel matrix for fast spectral embedding
Conference paper
Efficient classification of multi-label and imbalanced data using min-max modular classifiers
Conference paper
Efficient kernel feature extraction for massive data sets
Conference paper
Fast speaker adaption via maximum penalized likelihood kernel regression
Conference paper
Learning the kernel in mahalanobis one-class support vector machines
Conference paper
Locally adaptive classification piloted by uncertainty
Conference paper
Multimodal registration using the discrete wavelet frame transform
Conference paper
Simplifying mixture models through function approximation
Conference paper
Wavelet-based feature extraction for microarray data classification
Conference paper
Core vector machines: Fast SVM training on very large data sets
Article
Kernel eigenvoice speaker adaptation
Article
Accurate and Low-cost Location Estimation Using Kernels
Conference paper
Applying neighborhood consistency for fast clustering and kernel density estimation
Conference paper
Core vector regression for very large regression problems
Conference paper
Data-dependent kernels for high-dimensional data classification
Conference paper
Kernel relevant component analysis for distance metric learning
Conference paper
Page segmentation using mathematical morphology
Conference paper
Pattern de-noising based on support ector data description
Conference paper
Position estimation tor wireless sensor networks
Conference paper
Conference paper
Very large SVM training using core vector machines
Conference paper
Dissimilarity learning for nominal data
Article
Efficient hyperkernel learning using second-order cone programming
Article
Fusing images with different focuses using support vector machines
Article
Signal de-noising by improving soft thresholding on the dyadic wavelet transform
Article
The pre-image problem in kernel methods
Article
Conference paper
Bayesian inference on principal component analysis using reversible jump markov chain Monte Carlo
Conference paper
Eigenvoice speaker adaptation via composite kernel PCA
Conference paper
Incremental PCA based face recognition
Conference paper
Scaling up support vector data description by using core-sets
Conference paper
Speedup of Kernel Eigenvoice Speaker Adaptation by Embedded Kernel PCA
Conference paper
Study of various composite kernels for kernel eigenvoice speaker adaptation
Conference paper
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model
Conference paper
Text extraction using edge detection and morphological dilation
Conference paper
Using kernel PCA to improve eigenvoice speaker adaptation
Conference paper
Linear dependency between ε and the input noise in ε-support vector regression
Article
Mining customer product rating for personalized marketing
Article
Texture classification using the support vector machines
Article
Distance metric learning with kernels
Conference paper
Incremental eigen decomposition
Conference paper
Parametric distance metric learning with label information
Conference paper
The pre-image problem in kernel methods
Conference paper
Fusing images with multiple focuses using support vector machines
Article
Multifocus image fusion using artificial neural networks
Article
Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images
Article
Finding the pre-images in kernel principal component analysis
Conference paper
Fusing images with multiple focuses using support vector machines
Conference paper
Improving de-noising by coefficient de-noising and dyadic wavelet transform
Conference paper
Combination of images with diverse focuses using the spatial frequency
Article
Linear dependency between epsilon and the input noise in epsilon-support vector regression
Article
Applying the Bayesian evidence framework to nu-support vector regression
Conference paper
Bayesian support vector regression
Conference paper
The evidence framework applied to support vector machines
Article
An extended genetic rule induction algorithm
Conference paper
Conference paper
Rival penalized competitive learning for model-based sequence clustering
Conference paper
Moderating the outputs of support vector machine classifiers
Article
Integrating the evidence framework and the support vector machine
Conference paper
Moderating the outputs of support vector machine classifiers
Conference paper
Automated text categorization using support vector machine
Conference paper
Support vector mixture for classification and regression problems
Conference paper
Article
Objective functions for training new hidden units in constructive neural networks
Article
Use of bias term in projection pursuit learning improves approximation and convergence properties
Article
Bayesian regularization in constructive neural networks
Conference paper
Reference priors for neural networks: Laplace versus Gaussian
Conference paper
IMPROVING THE APPROXIMATION AND CONVERGENCE CAPABILITIES OF PROJECTION PURSUIT LEARNING
Article
Efficient Cross-Validation for Feedforward Neural Networks
Conference paper
A theoretically sound learning algorithm for constructive neural networks
Conference paper
Constructive Neural Networks : Some Practical Considerations
Conference paper
Experimental Analysis Of Input Weight Freezing In Constructive Neural Networks
Conference paper
COMP3211 | Fundamentals of Artificial Intelligence |
COMP4331 | Data Mining |
COMP6921T | Research Project |
RMBI2001 | Academic and Professional Development in Risk Management and Business Intelligence |
COMP4981 | Final Year Project |
COMP4981H | Final Year Thesis |
COMP4981 | Final Year Project |
COMP4981H | Final Year Thesis |
COMP6921T | Research Project |
MFIT5004 | Financial Data Mining |
MSBD5008 | Introduction to Social Computing |
RMBI2001 | Academic and Professional Development in Risk Management and Business Intelligence |
RMBI4980 | Risk Management and Business Intelligence Capstone Project I |
RMBI4990 | Risk Management and Business Intelligence Capstone Project II |
COMP4981 | Final Year Project |
COMP3211 | Fundamentals of Artificial Intelligence |
COMP4211 | Machine Learning |
COMP4981 | Final Year Project |
COMP6921T | Research Project |
RMBI2001 | Academic and Professional Development in Risk Management and Business Intelligence |
RMBI4980 | Risk Management and Business Intelligence Capstone Project I |
COMP4981 | Final Year Project |
COMP4981H | Final Year Thesis |
ANCHAL
(co-supervision)
Individualized Interdisciplinary Program
DELIKOURA, Iris
Individualized Interdisciplinary Program
HUANG, Ruping
Computer Science and Engineering
PANG, Ching Christie
(co-supervision)
Individualized Interdisciplinary Program
WU, Ze
Individualized Interdisciplinary Program
CHEUNG, Chi San
(co-supervision)
Individualized Interdisciplinary Program
TSUI, Yuk Hang
Individualized Interdisciplinary Program
GOU, Yunhao
Computer Science and Engineering
ZHU, Yiming
(co-supervision)
Individualized Interdisciplinary Program (Artificial Intelligence)
CHEN, Weiyu
Computer Science and Engineering
LIU, Zhili
Computer Science and Engineering
WEI, Yanbin
Computer Science and Engineering
YANG, Hansi
Computer Science and Engineering
CAO, Xilin
Computer Science and Engineering
CHAI, Wen Jye
Individualized Interdisciplinary Program
HOU, Kai Syun
Computer Science and Engineering
XU, Weiqing
Computer Science and Engineering
POON, Chung Hoo
Computer Science and Engineering
FUNG, Nai Chit
Computer Science and Engineering
JIANG, Weisen
Computer Science and Engineering( Completed in 2024 )
SHEN, Lifeng
(co-supervision)
Individualized Interdisciplinary Program (Artificial Intelligence)( Completed in 2024 )
SHI, Han
Computer Science and Engineering( Completed in 2022 )
YU, Jincheng
Computer Science and Engineering( Completed in 2024 )
YU, Runsheng
Computer Science and Engineering( Completed in 2024 )
CHAN, Lawrence Ki-on
Computer Science and Engineering( Completed in 2021 )