James Tin-yau Kwok
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Computer Science
James Tin-yau Kwok's Degrees
- PhD Computer Science University of California, Berkeley
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(Suggest an Edit or Addition)James Tin-yau Kwok's Published Works
Number of citations in a given year to any of this author's works
Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author
Published Works
- Domain Adaptation via Transfer Component Analysis (2009) (3131)
- Generalizing from a Few Examples: A Survey on Few-Shot Learning (2019) (1064)
- Core Vector Machines: Fast SVM Training on Very Large Data Sets (2005) (1060)
- Transfer Learning via Dimensionality Reduction (2008) (650)
- Constructive algorithms for structure learning in feedforward neural networks for regression problems (1997) (502)
- The pre-image problem in kernel methods (2003) (482)
- Generalizing from a Few Examples (2020) (479)
- Combination of images with diverse focuses using the spatial frequency (2001) (389)
- Asynchronous Distributed ADMM for Consensus Optimization (2014) (342)
- Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images (2002) (335)
- Improved Nyström low-rank approximation and error analysis (2008) (333)
- Multifocus image fusion using artificial neural networks (2002) (293)
- Mining customer product ratings for personalized marketing (2003) (269)
- Objective functions for training new hidden units in constructive neural networks (1997) (234)
- A novel incremental principal component analysis and its application for face recognition (2006) (225)
- Simpler core vector machines with enclosing balls (2007) (218)
- Texture classification using the support vector machines (2003) (217)
- Clustered Nyström Method for Large Scale Manifold Learning and Dimension Reduction (2010) (207)
- Maximum Margin Clustering Made Practical (2007) (197)
- Multi-Label Learning with Global and Local Label Correlation (2017) (195)
- Loss-aware Binarization of Deep Networks (2016) (192)
- Accelerated Gradient Method for Multi-task Sparse Learning Problem (2009) (189)
- Learning with Idealized Kernels (2003) (188)
- Efficient Multi-label Classification with Many Labels (2013) (188)
- Moderating the outputs of support vector machine classifiers (1999) (187)
- Accelerated Gradient Methods for Stochastic Optimization and Online Learning (2009) (179)
- Generalized Core Vector Machines (2006) (174)
- MultiLabel Classification on Tree- and DAG-Structured Hierarchies (2011) (170)
- Fusing images with different focuses using support vector machines (2004) (167)
- The evidence framework applied to support vector machines (2000) (158)
- Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing (2006) (140)
- Automated Text Categorization Using Support Vector Machine (1998) (140)
- Tighter and Convex Maximum Margin Clustering (2009) (134)
- Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning (2007) (132)
- Making Large-Scale Nyström Approximation Possible (2010) (127)
- Multiple Kernel Clustering (2009) (121)
- Fast Stochastic Alternating Direction Method of Multipliers (2013) (112)
- Linear dependency between ε and the input noise in ε-support vector regression (2003) (109)
- Loss-aware Weight Quantization of Deep Networks (2018) (109)
- Text detection in images using sparse representation with discriminative dictionaries (2010) (108)
- Building Sparse Multiple-Kernel SVM Classifiers (2009) (105)
- Semi-supervised learning using label mean (2009) (98)
- Prototype vector machine for large scale semi-supervised learning (2009) (97)
- Simplifying Mixture Models Through Function Approximation (2006) (96)
- Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVD (2015) (96)
- A Convex Method for Locating Regions of Interest with Multi-instance Learning (2009) (94)
- Distance metric learning with kernels (2003) (91)
- Efficient Sparse Modeling With Automatic Feature Grouping (2011) (91)
- Convex and scalable weakly labeled SVMs (2013) (89)
- Support vector mixture for classification and regression problems (1998) (87)
- A regularization framework for multiple-instance learning (2006) (86)
- Large-Scale Sparsified Manifold Regularization (2006) (85)
- Cost-Sensitive Semi-Supervised Support Vector Machine (2010) (85)
- Multilabel Classification with Label Correlations and Missing Labels (2014) (82)
- Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS (2020) (79)
- A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions (2011) (78)
- A Survey of Label-noise Representation Learning: Past, Present and Future (2020) (78)
- Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback (2019) (77)
- Parametric Distance Metric Learning with Label Information (2003) (75)
- Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels (2013) (74)
- Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network (2020) (72)
- Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers (2017) (71)
- Marginalized Multi-Instance Kernels (2007) (69)
- Time and space efficient spectral clustering via column sampling (2011) (68)
- Efficient hyperkernel learning using second-order cone programming (2004) (67)
- Efficient Learning of Timeseries Shapelets (2016) (67)
- Transferring Localization Models across Space (2008) (67)
- Convex Multitask Learning with Flexible Task Clusters (2012) (65)
- Density-Weighted Nyström Method for Computing Large Kernel Eigensystems (2009) (65)
- Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems (2016) (63)
- Dissimilarity learning for nominal data (2004) (61)
- Efficient Classification of Multi-label and Imbalanced Data using Min-Max Modular Classifiers (2006) (60)
- Searching to Exploit Memorization Effect in Learning with Noisy Labels (2020) (56)
- Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines (2008) (55)
- SVDD-Based Pattern Denoising (2007) (53)
- Learning to Predict from Crowdsourced Data (2014) (51)
- Fast-and-Light Stochastic ADMM (2016) (50)
- Kernel relevant component analysis for distance metric learning (2005) (50)
- Kernel eigenvoice speaker adaptation (2005) (49)
- Core Vector Regression for very large regression problems (2005) (48)
- Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression (2001) (46)
- Bayesian Support Vector Regression (2001) (45)
- Fast Low-Rank Matrix Learning with Nonconvex Regularization (2015) (44)
- Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity (2016) (44)
- Applying neighborhood consistency for fast clustering and kernel density estimation (2005) (43)
- Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification (2012) (41)
- Wavelet-Based Feature Extraction for Microarray Data Classification (2006) (41)
- Face recognition using spectral features (2007) (41)
- Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm (2017) (41)
- Very Large SVM Training using Core Vector Machines (2005) (41)
- End-to-end privacy control in service outsourcing of human intensive processes: A multi-layered Web service integration approach (2007) (41)
- Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines (2015) (39)
- Bayes-Optimal Hierarchical Multilabel Classification (2015) (39)
- Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares (2006) (38)
- Fast Second Order Stochastic Backpropagation for Variational Inference (2015) (37)
- Use of bias term in projection pursuit learning improves approximation and convergence properties (1996) (37)
- Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion (2015) (37)
- Surrogate maximization/minimization algorithms and extensions (2007) (36)
- Accurate Probability Calibration for Multiple Classifiers (2013) (36)
- Accurate and Low-cost Location Estimation Using Kernels (2005) (36)
- Towards Safe Semi-Supervised Learning for Multivariate Performance Measures (2016) (36)
- Rival penalized competitive learning for model-based sequence clustering (2000) (36)
- Efficient kernel feature extraction for massive data sets (2006) (34)
- Collaborative Filtering with Social Local Models (2017) (34)
- Matrix-Variate Factor Analysis and Its Applications (2008) (33)
- Online multiple instance learning with no regret (2010) (33)
- Efficient Neural Interaction Function Search for Collaborative Filtering (2019) (32)
- Scaling up support vector data description by using core-sets (2004) (32)
- Bayesian Regularization in Constructive Neural Networks (1996) (31)
- Position estimation for wireless sensor networks (2005) (30)
- Learning the Kernel in Mahalanobis One-Class Support Vector Machines (2006) (30)
- Density-Weighted Nystrm Method for Computing Large Kernel Eigensystems (2009) (29)
- Accelerated Stochastic Gradient Method for Composite Regularization (2014) (29)
- Normalization Helps Training of Quantized LSTM (2019) (29)
- Hierarchical Multilabel Classification with Minimum Bayes Risk (2012) (29)
- Effective Decoding in Graph Auto-Encoder using Triadic Closure (2019) (28)
- Bilinear Probabilistic Principal Component Analysis (2012) (28)
- Gradient Descent with Proximal Average for Nonconvex and Composite Regularization (2014) (28)
- SparseBERT: Rethinking the Importance Analysis in Self-attention (2021) (28)
- Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion (2017) (27)
- A Class of Single-Class Minimax Probability Machines for Novelty Detection (2007) (27)
- Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I (2010) (27)
- Multi-objective Neural Architecture Search via Predictive Network Performance Optimization (2019) (27)
- Accelerated Convergence Using Dynamic Mean Shift (2006) (26)
- Analysis of Quantized Models (2019) (26)
- A study of various composite kernels for kernel eigenvoice speaker adaptation (2004) (26)
- Experimental analysis of input weight freezing in constructive neural networks (1993) (25)
- Colorization by Patch-Based Local Low-Rank Matrix Completion (2015) (25)
- Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model (2004) (24)
- Asynchronous Distributed Semi-Stochastic Gradient Optimization (2015) (23)
- Model-based transductive learning of the kernel matrix (2006) (23)
- Scalable Online Convolutional Sparse Coding (2017) (23)
- Incremental eigen decomposition (2003) (23)
- Locally adaptive classification piloted by uncertainty (2006) (22)
- Fast Distributed Asynchronous SGD with Variance Reduction (2015) (22)
- Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting (2006) (22)
- Greedy Learning of Generalized Low-Rank Models (2016) (21)
- Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm (2004) (21)
- Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations (2019) (19)
- Revisiting Over-smoothing in BERT from the Perspective of Graph (2022) (19)
- Efficient cross-validation for feedforward neural networks (1995) (18)
- Integrating the evidence framework and the support vector machine (1999) (18)
- Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo (2004) (18)
- Block-quantized kernel matrix for fast spectral embedding (2006) (17)
- Incremental PCA based face recognition (2004) (16)
- Eigenvoice Speaker Adaptation via Composite Kernel PCA (2003) (16)
- Diversified SVM Ensembles for Large Data Sets (2006) (16)
- Unsupervised Maximum Margin Feature Selection with manifold regularization (2009) (16)
- om Low-Rank Approximation and Error Analysis (2008) (14)
- Time Series Anomaly Detection with Multiresolution Ensemble Decoding (2021) (14)
- Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition (2002) (14)
- Efficient Sample-based Neural Architecture Search with Learnable Predictor (2019) (14)
- Online Convolutional Sparse Coding (2017) (13)
- Maximum Penalized Likelihood Kernel Regression for Fast Adaptation (2009) (13)
- Applying the Bayesian Evidence Framework to \nu -Support Vector Regression (2001) (13)
- Multimodal Registration using the Discrete Wavelet Frame Transform (2006) (12)
- Pattern de-noising based on support vector data description (2005) (12)
- Finding the pre-images in kernel principal component analysis (2002) (12)
- Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006, Proceedings (2006) (12)
- Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA (2004) (11)
- TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation (2021) (10)
- Incorporating the Loss Function Into Discriminative Clustering of Structured Outputs (2010) (10)
- Follow the Moving Leader in Deep Learning (2017) (10)
- Side Information Fusion for Recommender Systems over Heterogeneous Information Network (2021) (9)
- Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction (2019) (9)
- Stochastic Variance-Reduced ADMM (2016) (9)
- Noniterative Sparse LS-SVM Based on Globally Representative Point Selection (2020) (9)
- Data-dependent kernels for high-dimensional data classification (2005) (9)
- Mining customer preference ratings for product recommendation using the support vector machine and the latent class model (2000) (9)
- Fusing Images with Multiple Focuses Using Support Vector Machines (2002) (8)
- Scalable Robust Matrix Factorization with Nonconvex Loss (2017) (8)
- Zero-shot learning with a partial set of observed attributes (2017) (7)
- Text extraction using edge detection and morphological dilation (2004) (7)
- Generalized Convolutional Sparse Coding With Unknown Noise (2019) (7)
- Efficient Kernel Learning from Side Information Using ADMM (2013) (7)
- Individuals and Groups (2015) (7)
- Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent (2015) (7)
- A Note on the Unification of Adaptive Online Learning (2017) (6)
- Simple randomized algorithms for online learning with kernels (2014) (6)
- Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings ... Vision, Pattern Recognition, and Graphics) (2008) (6)
- Learning with Heterogeneous Side Information Fusion for Recommender Systems (2018) (6)
- Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data (2018) (6)
- Maximum Margin Clustering with Multivariate Loss Function (2009) (6)
- Eigenvoice Speaker Adaptation via Composite Kernel Principal Component Analysis (2003) (5)
- Authors' Reply to the "Comments on the Core Vector Machines: Fast SVM Training on Very Large Data Sets" (2007) (5)
- More Efficient Accelerated Proximal Algorithm for Nonconvex Problems (2016) (5)
- Fast Speaker Adaption Via Maximum Penalized Likelihood Kernel Regression (2006) (5)
- Improving de-noising by coefficient de-noising and dyadic wavelet transform (2002) (5)
- Facial Image Reconstruction by SVDD-Based Pattern De-noising (2006) (5)
- Dropout's Dream Land: Generalization from Learned Simulators to Reality (2021) (4)
- Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval (2021) (4)
- A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning (2021) (4)
- Multi-Label learning in the independent label sub-spaces (2017) (4)
- Differential Private Stack Generalization with an Application to Diabetes Prediction. (2018) (4)
- Sliced Coordinate Analysis for Effective Dimension Reduction and Nonlinear Extensions (2008) (4)
- Towards end-to-end privacy control in the outsourcing of marketing activities: a web service integration solution (2005) (4)
- Bilinear Scoring Function Search for Knowledge Graph Learning (2021) (4)
- Online Convolutional Sparse Coding with Sample-Dependent Dictionary (2018) (4)
- Signal De-Noising by Improving Soft Thresholding on the Dyadic Wavelet Transform (2004) (3)
- Fast Learning with Nonconvex L1-2 Regularization (2016) (3)
- Low-Rank Matrix Learning Using Biconvex Surrogate Minimization (2019) (3)
- Incorporating cellular sorting structure for better prediction of protein subcellular locations (2011) (3)
- A novel distance-based classifier using convolution kernels and Euclidean embeddings (2002) (3)
- Sparse Modeling with Automatic Feature Grouping (2011) (2)
- Searching for Interaction Functions in Collaborative Filtering (2019) (2)
- Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18–22, 2020, Proceedings, Part V (2020) (2)
- SEEN: Few-Shot Classification with SElf-ENsemble (2021) (2)
- Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set (2018) (2)
- Spectral and Semidefinite Relaxation of the CLUHSIC Algorithm (2010) (2)
- Fast and accurate kernel density approximation using a divide-and-conquer approach (2010) (2)
- Using kernel PCA to improve eigenvoice speaker adaptation (2004) (2)
- Page segmentation using mathematical morphology (2005) (2)
- Improving the approximation and convergence capabilities of projection pursuit learning (1995) (2)
- Structural, Syntactic, and Statistical Pattern Recognition: Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, ... (Lecture Notes in Computer Science) (2006) (2)
- Searching a High Performance Feature Extractor for Text Recognition Network (2022) (2)
- Power Law in Sparsified Deep Neural Networks (2018) (2)
- Collaborative filtering via co-factorization of individuals and groups (2015) (2)
- Effective Meta-Regularization by Kernelized Proximal Regularization (2021) (1)
- Ensembles of Partially Trained SVMs with Multiplicative Updates (2007) (1)
- Fast Nonsmooth Regularized Risk Minimization with Continuation (2016) (1)
- Learning of Generalized Low-Rank Models: A Greedy Approach (2016) (1)
- General Convolutional Sparse Coding with Unknown Noise (2019) (1)
- om Method for Computing Large Kernel Eigensystems (2009) (1)
- Efficient Learning for Models with DAG-Structured Parameter Constraints (2013) (1)
- Manifold regularization for structured outputs via the joint kernel (2010) (1)
- Machine Learning (2015) (1)
- FasTer: Fast Tensor Completion with Nonconvex Regularization. (2018) (1)
- Incremental peA Based Face Recognition (2004) (1)
- Feedback Pyramid Attention Networks for Single Image Super-Resolution (2021) (1)
- AntNet: Deep Answer Understanding Network for Natural Reverse QA. (2019) (1)
- Domain Adaptation via Transfer Component Analysis — Source link (1)
- Probabilistic Kernel Matrix Learning with a Mixture Model of Kernels (2003) (1)
- Simplifying mixture Models through Function Approximation Simplifying Mixture Models through Function Approximation (2006) (1)
- Efficient Variance Reduction for Meta-learning (2022) (1)
- Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning (2019) (1)
- Structured clustering with automatic kernel adaptation (2011) (0)
- Linear Dependency betweenand the Input Noise in -Support Vector Regression (2001) (0)
- 2 Ordinal Label Aggregation by Minimax Conditional Entropy (2016) (0)
- Advances in Neural Networks--ISNN 2010: 7th International Symposium on Neural Networks, ISNN, Shanghai, China, June 6-9, 2010: proceedings (2010) (0)
- New transformation method in continuous particle swarm optimisation for feature selection (2022) (0)
- No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation (2023) (0)
- Adversarial Attack and Defense for Dehazing Networks (2023) (0)
- Fast Nonsmooth Regularized Risk Minimization with Continuation Shuai (2018) (0)
- Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization (2022) (0)
- Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space (2019) (0)
- Efficient Robust Matrix Factorization with Nonconvex Loss (2017) (0)
- Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part I (2020) (0)
- Position estimation tor wireless sensor networks (2005) (0)
- ICPR2006 Organizing Committee (2006) (0)
- Learning from High-Dimensional Data in Multitask/Multilabel Classification (2013) (0)
- Low-Rank Matrix Learning using Biconvex Surrogate Minimization (submitted) (2016) (0)
- Reference priors for neural networks: Laplace versus Gaussian (1996) (0)
- Bioinformatics and Biomedical Applications-Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares (2006) (0)
- Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part III (2020) (0)
- Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition (2023) (0)
- Scaling up support vector machines (2007) (0)
- AlignVE: Visual Entailment Recognition Based on Alignment Relations (2022) (0)
- Policy Tree Network (2019) (0)
- L OSS-AWARE B INARIZATION OF D EEP N ETWORKS (2017) (0)
- Neural Information Processing: 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23–27, 2020, Proceedings, Part II (2020) (0)
- Selected papers from the 2011 International Conference on Neural Information Processing (ICONIP 2011) (2014) (0)
- Title Bilinear probabilistic principal component analysis (2012) (0)
- Query Rewriting in TaoBao Search (2022) (0)
- Mining ustomer preferen e ratings for produ t re ommendation using the support ve tor ma hine and the latent lass model (2018) (0)
- A brief introduction to the special issue for ISNN2010 (2012) (0)
- Corrigendum to "Multi-label learning in the independent label sub-spaces" [Pattern Recognition Letters 97(2017) 8-12] (2018) (0)
- Convexity , Surrogate Functions and Iterative Optimization in Multi-class Logistic Regression Models (2004) (0)
- Book and Media Review Editor (2008) (0)
- A novel family of subspace methods---protoface and its kernel version (2002) (0)
- Power Law in Deep Neural Networks: Sparse Network Generation and Continual Learning With Preferential Attachment. (2022) (0)
- Tensorizing Subgraph Search in the Supernet (2021) (0)
- A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Completion (2020) (0)
- Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning (2023) (0)
- R EVISITING O VER - SMOOTHING IN BERT FROM THE P ERSPECTIVE OF G RAPH (2022) (0)
- Accurate Integration of Aerosol Predictions by Smoothing on a Manifold (2014) (0)
- Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering (2016) (0)
- Special issue: First International Conference on Big Data and Smart Computing (BigComp2014) (2016) (0)
- Efficient Low-Rank Matrix Learning by Factorizable Nonconvex Regularization (2020) (0)
- Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution (2021) (0)
- Pyramidal dense attention networks for single image super-resolution (2022) (0)
- Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration (2019) (0)
- Flexible Nonparametric Kernel Learning with Different Loss Functions (2013) (0)
- Efficient Low-Rank Semidefinite Programming With Robust Loss Functions (2019) (0)
- L OSS-AWARE W EIGHT Q UANTIZATION OF D EEP N ET-WORKS (2018) (0)
- Fast Learning of Nonconvex `1-2-Regularizer using the Proximal Gradient Algorithm (2017) (0)
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James Tin-yau Kwok is affiliated with the following schools:
