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ORCIDJames T. Kwok
James Tin-Yau Kwok
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- affiliation: Hong Kong University of Science and Technology
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2020 – today
- 2024
[j87]Hansi Yang
, Quanming Yao, Bo Han, James T. Kwok:
Searching to Exploit Memorization Effect in Deep Learning With Noisy Labels. IEEE Trans. Pattern Anal. Mach. Intell. 46(12): 7833-7849 (2024)- [j86]Biwei Cao, Jiuxin Cao, Bo Liu, Jie Gui, Jun Zhou, Yuan Yan Tang, James Tin-Yau Kwok:
Response Generation in Social Network With Topic and Emotion Constraints. IEEE Trans. Comput. Soc. Syst. 11(5): 6592-6604 (2024) - [j85]Jie Gui, Xiaofeng Cong, Chengwei Peng, Yuan Yan Tang, James Tin-Yau Kwok:
Fooling the Image Dehazing Models by First Order Gradient. IEEE Trans. Circuits Syst. Video Technol. 34(7): 6265-6278 (2024) - [j84]Yaofo Chen, Yong Guo, Daihai Liao, Fanbing Lv, Hengjie Song, James Tin-Yau Kwok, Mingkui Tan:
Automated Dominative Subspace Mining for Efficient Neural Architecture Search. IEEE Trans. Circuits Syst. Video Technol. 34(10): 9281-9297 (2024) - [j83]Yifan Wang, Jie Gui, Yuan Yan Tang, James Tin-Yau Kwok:
CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition. IEEE Trans. Inf. Forensics Secur. 19: 7810-7823 (2024) - [j82]Yu-Xin Zhang, Jie Gui, James Tin-Yau Kwok:
Constructing Diverse Inlier Consistency for Partial Point Cloud Registration. IEEE Trans. Image Process. 33: 6535-6549 (2024) - [j81]Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, James T. Kwok:
A Survey on Time-Series Pre-Trained Models. IEEE Trans. Knowl. Data Eng. 36(12): 7536-7555 (2024) - [j80]Jie Gui, Xiaofeng Cong, Lei He, Yuan Yan Tang, James Tin-Yau Kwok:
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding. IEEE Trans. Multim. 26: 4819-4830 (2024) - [j79]Fan Feng, Lu Hou, Qi She, Rosa H. M. Chan, James T. Kwok:
Power Law in Deep Neural Networks: Sparse Network Generation and Continual Learning With Preferential Attachment. IEEE Trans. Neural Networks Learn. Syst. 35(7): 8999-9013 (2024) - [c162]Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James T. Kwok:
Forward-Backward Reasoning in Large Language Models for Mathematical Verification. ACL (Findings) 2024: 6647-6661 - [c161]Tao Li, Weisen Jiang, Fanghui Liu, Xiaolin Huang, James T. Kwok:
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy. ECCV (65) 2024: 342-359 - [c160]Yunhao Gou, Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T. Kwok, Yu Zhang:
Eyes Closed, Safety on: Protecting Multimodal LLMs via Image-to-Text Transformation. ECCV (17) 2024: 388-404 - [c159]Zhili Liu, Kai Chen, Yifan Zhang, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T. Kwok:
Implicit Concept Removal of Diffusion Models. ECCV (21) 2024: 457-473 - [c158]Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Zhongdao Wang, James T. Kwok, Ping Luo, Huchuan Lu, Zhenguo Li:
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis. ICLR 2024 - [c157]Lifeng Shen, Weiyu Chen, James T. Kwok:
Multi-Resolution Diffusion Models for Time Series Forecasting. ICLR 2024 - [c156]Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu:
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. ICLR 2024 - [c155]Weiyu Chen, James T. Kwok:
Efficient Pareto Manifold Learning with Low-Rank Structure. ICML 2024 - [c154]Runsheng Yu, Youzhi Zhang, James T. Kwok:
Improving Sharpness-Aware Minimization by Lookahead. ICML 2024 - [i74]Yanbin Wei, Shuai Fu, Weisen Jiang, James T. Kwok, Yu Zhang:
Rendering Graphs for Graph Reasoning in Multimodal Large Language Models. CoRR abs/2402.02130 (2024) - [i73]Yanbin Wei, Qiushi Huang, James T. Kwok, Yu Zhang:
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. CoRR abs/2402.02389 (2024) - [i72]Zhili Liu, Kai Chen, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, James T. Kwok:
Task-customized Masked AutoEncoder via Mixture of Cluster-conditional Experts. CoRR abs/2402.05382 (2024) - [i71]Yunhao Gou, Kai Chen, Zhili Liu, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T. Kwok, Yu Zhang:
Eyes Closed, Safety On: Protecting Multimodal LLMs via Image-to-Text Transformation. CoRR abs/2403.09572 (2024) - [i70]Zhili Liu, Yunhao Gou, Kai Chen, Lanqing Hong, Jiahui Gao, Fei Mi, Yu Zhang, Zhenguo Li, Xin Jiang, Qun Liu, James T. Kwok:
Mixture of insighTful Experts (MoTE): The Synergy of Thought Chains and Expert Mixtures in Self-Alignment. CoRR abs/2405.00557 (2024) - [i69]Runsheng Yu, Yong Wang, Xiaoqi Jiao, Youzhi Zhang, James T. Kwok:
Direct Alignment of Language Models via Quality-Aware Self-Refinement. CoRR abs/2405.21040 (2024) - [i68]Lifeng Shen, Jincheng Yu, Hansi Yang, James T. Kwok:
Mixup Augmentation with Multiple Interpolations. CoRR abs/2406.01417 (2024) - [i67]Hansi Yang, James T. Kwok:
Communication-Efficient and Privacy-Preserving Decentralized Meta-Learning. CoRR abs/2406.13183 (2024) - [i66]Tao Li, Weisen Jiang, Fanghui Liu, Xiaolin Huang, James T. Kwok:
Learning Scalable Model Soup on a Single GPU: An Efficient Subspace Training Strategy. CoRR abs/2407.03641 (2024) - [i65]Weiyu Chen, James T. Kwok:
Efficient Pareto Manifold Learning with Low-Rank Structure. CoRR abs/2407.20734 (2024) - [i64]Siyu Zhai, Zhibo He, Xiaofeng Cong, Junming Hou, Jie Gui, Jian Wei You, Xin Gong, James Tin-Yau Kwok, Yuan Yan Tang:
Unrevealed Threats: A Comprehensive Study of the Adversarial Robustness of Underwater Image Enhancement Models. CoRR abs/2409.06420 (2024) - [i63]Yifan Wang, Jie Gui, Yuan Yan Tang, James Tin-Yau Kwok:
CFVNet: An End-to-End Cancelable Finger Vein Network for Recognition. CoRR abs/2409.14774 (2024) - [i62]Xiaofeng Cong, Jing Zhang, Yeying Jin, Junming Hou, Yu Zhao, Jie Gui, James Tin-Yau Kwok, Yuan Yan Tang:
Underwater Organism Color Enhancement via Color Code Decomposition, Adaptation and Interpolation. CoRR abs/2409.19685 (2024) - [i61]Shuhao Chen, Weisen Jiang, Baijiong Lin, James T. Kwok, Yu Zhang:
RouterDC: Query-Based Router by Dual Contrastive Learning for Assembling Large Language Models. CoRR abs/2409.19886 (2024) - 2023
- [j78]Yongqi Zhang, Quanming Yao, James T. Kwok:
Bilinear Scoring Function Search for Knowledge Graph Learning. IEEE Trans. Pattern Anal. Mach. Intell. 45(2): 1458-1473 (2023) - [j77]Hui Zhang, Quanming Yao, James T. Kwok, Xiang Bai:
Searching a High Performance Feature Extractor for Text Recognition Network. IEEE Trans. Pattern Anal. Mach. Intell. 45(5): 6231-6246 (2023) - [j76]Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei:
Feedback Pyramid Attention Networks for Single Image Super-Resolution. IEEE Trans. Circuits Syst. Video Technol. 33(9): 4881-4892 (2023) - [j75]Jidong Ge, Yuxiang Liu, Jie Gui, Lanting Fang, Ming Lin, James Tin-Yau Kwok, Liguo Huang, Bin Luo:
Learning the Relation Between Similarity Loss and Clustering Loss in Self-Supervised Learning. IEEE Trans. Image Process. 32: 3442-3454 (2023) - [j74]Biwei Cao, Jiuxin Cao, Jie Gui, Jiayun Shen, Bo Liu, Lei He, Yuan Yan Tang, James Tin-Yau Kwok:
AlignVE: Visual Entailment Recognition Based on Alignment Relations. IEEE Trans. Multim. 25: 7378-7387 (2023) - [c153]Hansi Yang, Yongqi Zhang, Quanming Yao, James T. Kwok:
Positive-Unlabeled Node Classification with Structure-aware Graph Learning. CIKM 2023: 4390-4394 - [c152]Yunhao Gou, Tom Ko, Hansi Yang, James T. Kwok, Yu Zhang, Mingxuan Wang:
Leveraging per Image-Token Consistency for Vision-Language Pre-training. CVPR 2023: 19155-19164 - [c151]Yanbin Wei, Qiushi Huang, Yu Zhang, James T. Kwok:
KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. EMNLP (Findings) 2023: 8667-8683 - [c150]Yuhui Guo, Xun Liang, James T. Kwok, Xiangping Zheng, Bo Wu, Yuefeng Ma:
Cross-Modal Matching and Adaptive Graph Attention Network for RGB-D Scene Recognition. ICASSP 2023: 1-5 - [c149]Xinchi Deng, Han Shi, Runhui Huang, Changlin Li, Hang Xu, Jianhua Han, James T. Kwok, Shen Zhao, Wei Zhang, Xiaodan Liang:
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training. ICCV 2023: 22121-22132 - [c148]Weisen Jiang, Hansi Yang, Yu Zhang, James T. Kwok:
An Adaptive Policy to Employ Sharpness-Aware Minimization. ICLR 2023 - [c147]Zhili Liu, Kai Chen, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, James T. Kwok:
Task-customized Masked Autoencoder via Mixture of Cluster-conditional Experts. ICLR 2023 - [c146]Runsheng Yu, Weiyu Chen, Xinrun Wang, James T. Kwok:
Enhancing Meta Learning via Multi-Objective Soft Improvement Functions. ICLR 2023 - [c145]Weisen Jiang, Yu Zhang, James T. Kwok:
Effective Structured Prompting by Meta-Learning and Representative Verbalizer. ICML 2023: 15186-15199 - [c144]Lifeng Shen, James T. Kwok:
Non-autoregressive Conditional Diffusion Models for Time Series Prediction. ICML 2023: 31016-31029 - [c143]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Iterative Machine Teaching. ICML 2023: 40851-40870 - [c142]Zhenqian Shen, Hansi Yang, Yong Li, James T. Kwok, Quanming Yao:
Efficient Hyper-parameter Optimization with Cubic Regularization. NeurIPS 2023 - [c141]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Teaching for Multiple Learners. NeurIPS 2023 - [i60]Jidong Ge, Yuxiang Liu, Jie Gui, Lanting Fang, Ming Lin, James Tin-Yau Kwok, LiGuo Huang, Bin Luo:
Learning the Relation between Similarity Loss and Clustering Loss in Self-Supervised Learning. CoRR abs/2301.03041 (2023) - [i59]Jie Gui, Xiaofeng Cong, Chengwei Peng, Yuan Yan Tang, James Tin-Yau Kwok:
Adversarial Attack and Defense for Dehazing Networks. CoRR abs/2303.17255 (2023) - [i58]Biwei Cao, Lulu Hua, Jiuxin Cao, Jie Gui, Bo Liu, James Tin-Yau Kwok:
No Place to Hide: Dual Deep Interaction Channel Network for Fake News Detection based on Data Augmentation. CoRR abs/2303.18049 (2023) - [i57]Weisen Jiang, Hansi Yang, Yu Zhang, James T. Kwok:
An Adaptive Policy to Employ Sharpness-Aware Minimization. CoRR abs/2304.14647 (2023) - [i56]Qianli Ma, Zhen Liu, Zhenjing Zheng, Ziyang Huang, Siying Zhu, Zhongzhong Yu, James T. Kwok:
A Survey on Time-Series Pre-Trained Models. CoRR abs/2305.10716 (2023) - [i55]Weisen Jiang, Yu Zhang, James T. Kwok:
Effective Structured Prompting by Meta-Learning and Representative Verbalizer. CoRR abs/2306.00618 (2023) - [i54]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Iterative Machine Teaching. CoRR abs/2306.03007 (2023) - [i53]Lifeng Shen, James T. Kwok:
Non-autoregressive Conditional Diffusion Models for Time Series Prediction. CoRR abs/2306.05043 (2023) - [i52]Jie Gui, Xiaofeng Cong, Lei He, Yuan Yan Tang, James Tin-Yau Kwok:
Illumination Controllable Dehazing Network based on Unsupervised Retinex Embedding. CoRR abs/2306.05675 (2023) - [i51]Weisen Jiang, Han Shi, Longhui Yu, Zhengying Liu, Yu Zhang, Zhenguo Li, James T. Kwok:
Forward-Backward Reasoning in Large Language Models for Verification. CoRR abs/2308.07758 (2023) - [i50]Xinchi Deng, Han Shi, Runhui Huang, Changlin Li, Hang Xu, Jianhua Han, James T. Kwok, Shen Zhao, Wei Zhang, Xiaodan Liang:
GrowCLIP: Data-aware Automatic Model Growing for Large-scale Contrastive Language-Image Pre-training. CoRR abs/2308.11331 (2023) - [i49]Longhui Yu, Weisen Jiang, Han Shi, Jincheng Yu, Zhengying Liu, Yu Zhang, James T. Kwok, Zhenguo Li, Adrian Weller, Weiyang Liu:
MetaMath: Bootstrap Your Own Mathematical Questions for Large Language Models. CoRR abs/2309.12284 (2023) - [i48]Yulong Zhang, Shuhao Chen, Weisen Jiang, Yu Zhang, Jiangang Lu, James T. Kwok:
Domain-Guided Conditional Diffusion Model for Unsupervised Domain Adaptation. CoRR abs/2309.14360 (2023) - [i47]Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao, Enze Xie, Yue Wu, Zhongdao Wang, James T. Kwok, Ping Luo, Huchuan Lu, Zhenguo Li:
PixArt-α: Fast Training of Diffusion Transformer for Photorealistic Text-to-Image Synthesis. CoRR abs/2310.00426 (2023) - [i46]Weisen Jiang, Baijiong Lin, Han Shi, Yu Zhang, Zhenguo Li, James T. Kwok:
Effective and Parameter-Efficient Reusing Fine-Tuned Models. CoRR abs/2310.01886 (2023) - [i45]Zhili Liu, Kai Chen, Yifan Zhang, Jianhua Han, Lanqing Hong, Hang Xu, Zhenguo Li, Dit-Yan Yeung, James T. Kwok:
Geom-Erasing: Geometry-Driven Removal of Implicit Concept in Diffusion Models. CoRR abs/2310.05873 (2023) - [i44]Hansi Yang, Yongqi Zhang, Quanming Yao, James T. Kwok:
Positive-Unlabeled Node Classification with Structure-aware Graph Learning. CoRR abs/2310.13538 (2023) - [i43]Mingwei Xu, Xiaofeng Cao, Ivor W. Tsang, James T. Kwok:
Aggregation Weighting of Federated Learning via Generalization Bound Estimation. CoRR abs/2311.05936 (2023) - [i42]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Teaching for Multiple Learners. CoRR abs/2311.10318 (2023) - [i41]Yunhao Gou, Zhili Liu, Kai Chen, Lanqing Hong, Hang Xu, Aoxue Li, Dit-Yan Yeung, James T. Kwok, Yu Zhang:
Mixture of Cluster-conditional LoRA Experts for Vision-language Instruction Tuning. CoRR abs/2312.12379 (2023) - 2022
- [j73]Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei:
Pyramidal dense attention networks for single image super-resolution. IET Image Process. 16(12): 3247-3257 (2022) - [j72]Kangshun Li, Dunmin Chen, Zhaolian Zeng, Guang Chen, James Tin-Yau Kwok:
New transformation method in continuous particle swarm optimisation for feature selection. Int. J. Wirel. Mob. Comput. 22(2): 114-124 (2022) - [j71]Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok:
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. J. Mach. Learn. Res. 23: 136:1-136:60 (2022) - [j70]Quanming Yao, Hansi Yang, En-Liang Hu, James T. Kwok:
Efficient Low-Rank Semidefinite Programming With Robust Loss Functions. IEEE Trans. Pattern Anal. Mach. Intell. 44(10): 6153-6168 (2022) - [c140]Sen Li, Fuyu Lv, Taiwei Jin, Guiyang Li, Yukun Zheng, Tao Zhuang, Qingwen Liu, Xiaoyi Zeng, James T. Kwok, Qianli Ma:
Query Rewriting in TaoBao Search. CIKM 2022: 3262-3271 - [c139]Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong, Stephen M. S. Lee, James T. Kwok:
Revisiting Over-smoothing in BERT from the Perspective of Graph. ICLR 2022 - [c138]Weisen Jiang, James T. Kwok, Yu Zhang:
Subspace Learning for Effective Meta-Learning. ICML 2022: 10177-10194 - [c137]Hansi Yang, James T. Kwok:
Efficient Variance Reduction for Meta-learning. ICML 2022: 25070-25095 - [i40]Han Shi, Jiahui Gao, Hang Xu, Xiaodan Liang, Zhenguo Li, Lingpeng Kong, Stephen M. S. Lee, James T. Kwok:
Revisiting Over-smoothing in BERT from the Perspective of Graph. CoRR abs/2202.08625 (2022) - [i39]Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok:
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. CoRR abs/2205.03059 (2022) - [i38]Hui Zhang, Quanming Yao, James T. Kwok, Xiang Bai:
Searching a High-Performance Feature Extractor for Text Recognition Network. CoRR abs/2209.13139 (2022) - [i37]Biwei Cao, Jiuxin Cao, Jie Gui, Jiayun Shen, Bo Liu, Lei He, Yuan Yan Tang, James Tin-Yau Kwok:
AlignVE: Visual Entailment Recognition Based on Alignment Relations. CoRR abs/2211.08736 (2022) - [i36]Yunhao Gou, Tom Ko, Hansi Yang, James T. Kwok, Yu Zhang, Mingxuan Wang:
Leveraging per Image-Token Consistency for Vision-Language Pre-training. CoRR abs/2211.15398 (2022) - 2021
- [j69]Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni:
Generalizing from a Few Examples: A Survey on Few-shot Learning. ACM Comput. Surv. 53(3): 63:1-63:34 (2021) - [j68]Huan Zhao, Quanming Yao, Yangqiu Song, James T. Kwok, Dik Lun Lee:
Side Information Fusion for Recommender Systems over Heterogeneous Information Network. ACM Trans. Knowl. Discov. Data 15(4): 60:1-60:32 (2021) - [j67]Yuan Cao, Heng Qi, Jie Gui, Keqiu Li, Yuan Yan Tang, James Tin-Yau Kwok:
Learning to Hash With Dimension Analysis Based Quantizer for Image Retrieval. IEEE Trans. Multim. 23: 3907-3918 (2021) - [j66]Yuefeng Ma, Xun Liang, Gang Sheng, James T. Kwok, Maoli Wang, Guangshun Li:
Noniterative Sparse LS-SVM Based on Globally Representative Point Selection. IEEE Trans. Neural Networks Learn. Syst. 32(2): 788-798 (2021) - [c136]Lifeng Shen, Zhongzhong Yu, Qianli Ma, James T. Kwok:
Time Series Anomaly Detection with Multiresolution Ensemble Decoding. AAAI 2021: 9567-9575 - [c135]Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang, Zhenguo Li, James Tin-Yau Kwok:
SparseBERT: Rethinking the Importance Analysis in Self-attention. ICML 2021: 9547-9557 - [c134]Weisen Jiang, Yu Zhang, James T. Kwok:
SEEN: Few-Shot Classification with SElf-ENsemble. IJCNN 2021: 1-8 - [c133]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. NeurIPS 2021: 20970-20982 - [c132]Weisen Jiang, James T. Kwok, Yu Zhang:
Effective Meta-Regularization by Kernelized Proximal Regularization. NeurIPS 2021: 26212-26222 - [c131]Zac Wellmer, James T. Kwok:
Dropout's Dream Land: Generalization from Learned Simulators to Reality. ECML/PKDD (1) 2021: 255-270 - [c130]Yaqing Wang, Quanming Yao, James T. Kwok:
A Scalable, Adaptive and Sound Nonconvex Regularizer for Low-rank Matrix Learning. WWW 2021: 1798-1808 - [i35]Hansi Yang, Quanming Yao, James T. Kwok:
Tensorizing Subgraph Search in the Supernet. CoRR abs/2101.01078 (2021) - [i34]Han Shi, Jiahui Gao, Xiaozhe Ren, Hang Xu, Xiaodan Liang, Zhenguo Li, James T. Kwok:
SparseBERT: Rethinking the Importance Analysis in Self-attention. CoRR abs/2102.12871 (2021) - [i33]Haoang Chi, Feng Liu, Wenjing Yang, Long Lan, Tongliang Liu, Bo Han, William K. Cheung, James T. Kwok:
TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation. CoRR abs/2106.06326 (2021) - [i32]Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei:
Feedback Pyramid Attention Networks for Single Image Super-Resolution. CoRR abs/2106.06966 (2021) - [i31]Huapeng Wu, Jie Gui, Jun Zhang, James T. Kwok, Zhihui Wei:
Pyramidal Dense Attention Networks for Lightweight Image Super-Resolution. CoRR abs/2106.06996 (2021) - [i30]Zac Wellmer, James T. Kwok:
Dropout's Dream Land: Generalization from Learned Simulators to Reality. CoRR abs/2109.08342 (2021) - 2020
- [j65]Yaqing Wang, James T. Kwok, Lionel M. Ni:
Generalized Convolutional Sparse Coding With Unknown Noise. IEEE Trans. Image Process. 29: 5386-5395 (2020) - [c129]Han Shi, Haozheng Fan, James T. Kwok:
Effective Decoding in Graph Auto-Encoder Using Triadic Closure. AAAI 2020: 906-913 - [c128]Quanming Yao, Hansi Yang, Bo Han, Gang Niu, James Tin-Yau Kwok:
Searching to Exploit Memorization Effect in Learning with Noisy Labels. ICML 2020: 10789-10798 - [c127]Rishabh Mehrotra, Ben Carterette, Yong Li, Quanming Yao, Chen Gao, James T. Kwok, Qiang Yang, Isabelle Guyon:
Advances in Recommender Systems: From Multi-stakeholder Marketplaces to Automated RecSys. KDD 2020: 3533-3534 - [c126]Lifeng Shen, Zhuocong Li, James T. Kwok:
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network. NeurIPS 2020 - [c125]Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang:
Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS. NeurIPS 2020 - [c124]Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li, Cho-Jui Hsieh:
Efficient Neural Interaction Function Search for Collaborative Filtering. WWW 2020: 1660-1670 - [p2]Xiawei Guo, Quanming Yao, James T. Kwok, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang:
Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. Federated Learning 2020: 269-283 - [e12]Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18-22, 2020, Proceedings, Part IV. Communications in Computer and Information Science 1332, Springer 2020, ISBN 978-3-030-63819-1 [contents] - [e11]Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 18-22, 2020, Proceedings, Part V. Communications in Computer and Information Science 1333, Springer 2020, ISBN 978-3-030-63822-1 [contents] - [e10]Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part I. Lecture Notes in Computer Science 12532, Springer 2020, ISBN 978-3-030-63829-0 [contents] - [e9]Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part II. Lecture Notes in Computer Science 12533, Springer 2020, ISBN 978-3-030-63832-0 [contents] - [e8]Haiqin Yang, Kitsuchart Pasupa, Andrew Chi-Sing Leung, James T. Kwok, Jonathan H. Chan, Irwin King:
Neural Information Processing - 27th International Conference, ICONIP 2020, Bangkok, Thailand, November 23-27, 2020, Proceedings, Part III. Lecture Notes in Computer Science 12534, Springer 2020, ISBN 978-3-030-63835-1 [contents] - [i29]Yaqing Wang, Quanming Yao, James T. Kwok:
Efficient Low-Rank Matrix Learning by Factorizable Nonconvex Regularization. CoRR abs/2008.06542 (2020) - [i28]Bo Han, Quanming Yao, Tongliang Liu, Gang Niu, Ivor W. Tsang, James T. Kwok, Masashi Sugiyama:
A Survey of Label-noise Representation Learning: Past, Present and Future. CoRR abs/2011.04406 (2020)
2010 – 2019
- 2019
- [j64]Quanming Yao, James T. Kwok, Taifeng Wang, Tie-Yan Liu:
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers. IEEE Trans. Pattern Anal. Mach. Intell. 41(11): 2628-2643 (2019) - [j63]Quanming Yao, James T. Kwok:
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion. IEEE Trans. Knowl. Data Eng. 31(9): 1665-1679 (2019) - [j62]En-Liang Hu, James T. Kwok:
Low-Rank Matrix Learning Using Biconvex Surrogate Minimization. IEEE Trans. Neural Networks Learn. Syst. 30(11): 3517-3527 (2019) - [c123]Lu Hou, Ruiliang Zhang, James T. Kwok:
Analysis of Quantized Models. ICLR (Poster) 2019 - [c122]Quanming Yao, James Tin-Yau Kwok, Bo Han:
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. ICML 2019: 7035-7044 - [c121]Quanming Yao, Xiawei Guo, James T. Kwok, Wei-Wei Tu, Yuqiang Chen, Wenyuan Dai, Qiang Yang:
Privacy-Preserving Stacking with Application to Cross-organizational Diabetes Prediction. IJCAI 2019: 4114-4120 - [c120]Minsam Kim, James T. Kwok:
Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration. IJCNN 2019: 1-8 - [c119]Lu Hou, Jinhua Zhu, James T. Kwok, Fei Gao, Tao Qin, Tie-Yan Liu:
Normalization Helps Training of Quantized LSTM. NeurIPS 2019: 7344-7354 - [c118]Shuai Zheng, Ziyue Huang, James T. Kwok:
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. NeurIPS 2019: 11446-11456 - [c117]Zac Wellmer, James T. Kwok:
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space. ECML/PKDD (3) 2019: 118-133 - [i27]Yaqing Wang, James T. Kwok, Lionel M. Ni:
General Convolutional Sparse Coding with Unknown Noise. CoRR abs/1903.03253 (2019) - [i26]Shuai Zheng, James T. Kwok:
Blockwise Adaptivity: Faster Training and Better Generalization in Deep Learning. CoRR abs/1905.09899 (2019) - [i25]Shuai Zheng, Ziyue Huang, James T. Kwok:
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. CoRR abs/1905.10936 (2019) - [i24]Quanming Yao, Xiangning Chen, James T. Kwok, Yong Li:
Searching for Interaction Functions in Collaborative Filtering. CoRR abs/1906.12091 (2019) - [i23]Zac Wellmer, James T. Kwok:
Policy Prediction Network: Model-Free Behavior Policy with Model-Based Learning in Continuous Action Space. CoRR abs/1909.07373 (2019) - [i22]Han Shi, Renjie Pi, Hang Xu, Zhenguo Li, James T. Kwok, Tong Zhang:
Multi-objective Neural Architecture Search via Predictive Network Performance Optimization. CoRR abs/1911.09336 (2019) - [i21]Han Shi, Haozheng Fan, James T. Kwok:
Effective Decoding in Graph Auto-Encoder using Triadic Closure. CoRR abs/1911.11322 (2019) - 2018
- [j61]Elham J. Barezi, James T. Kwok, Hamid R. Rabiee:
Corrigendum to "Multi-label learning in the independent label sub-spaces" [Pattern Recognition Letters 97(2017) 8-12]. Pattern Recognit. Lett. 112: 152 (2018) - [j60]Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni:
Scalable Online Convolutional Sparse Coding. IEEE Trans. Image Process. 27(10): 4850-4859 (2018) - [j59]Yue Zhu, James T. Kwok, Zhi-Hua Zhou:
Multi-Label Learning with Global and Local Label Correlation. IEEE Trans. Knowl. Data Eng. 30(6): 1081-1094 (2018) - [j58]Yuefeng Ma, Xun Liang, James T. Kwok, Jianping Li, Xiaoping Zhou, Haiyan Zhang:
Fast-Solving Quasi-Optimal LS-S3VM Based on an Extended Candidate Set. IEEE Trans. Neural Networks Learn. Syst. 29(4): 1120-1131 (2018) - [c116]Lu Hou, James T. Kwok:
Loss-aware Weight Quantization of Deep Networks. ICLR (Poster) 2018 - [c115]Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, Lionel M. Ni:
Online Convolutional Sparse Coding with Sample-Dependent Dictionary. ICML 2018: 5196-5205 - [c114]Shuai Zheng, James Tin-Yau Kwok:
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data. ICML 2018: 5927-5935 - [c113]Quanming Yao, James T. Kwok:
Scalable Robust Matrix Factorization with Nonconvex Loss. NeurIPS 2018: 5066-5075 - [i20]Huan Zhao, Quanming Yao, Yangqiu Song, James T. Kwok, Dik Lun Lee:
Learning with Heterogeneous Side Information Fusion for Recommender Systems. CoRR abs/1801.02411 (2018) - [i19]Lu Hou, James T. Kwok:
Loss-aware Weight Quantization of Deep Networks. CoRR abs/1802.08635 (2018) - [i18]Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni:
Online Convolutional Sparse Coding with Sample-Dependent Dictionary. CoRR abs/1804.10366 (2018) - [i17]Lu Hou, James T. Kwok:
Power Law in Sparsified Deep Neural Networks. CoRR abs/1805.01891 (2018) - [i16]Shuai Zheng, James T. Kwok:
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data. CoRR abs/1806.02927 (2018) - 2017
- [j57]Quanming Yao, James T. Kwok:
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. J. Mach. Learn. Res. 18: 179:1-179:52 (2017) - [j56]Elham J. Barezi, James T. Kwok, Hamid R. Rabiee:
Multi-Label learning in the independent label sub-spaces. Pattern Recognit. Lett. 97: 8-12 (2017) - [j55]Wenwu He, James Tin-Yau Kwok, Ji Zhu, Yang Liu:
A Note on the Unification of Adaptive Online Learning. IEEE Trans. Neural Networks Learn. Syst. 28(5): 1178-1191 (2017) - [c112]Xiawei Guo, Quanming Yao, James Tin-Yau Kwok:
Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm. AAAI 2017: 1948-1954 - [c111]Huan Zhao, Quanming Yao, James T. Kwok, Dik Lun Lee:
Collaborative Filtering with Social Local Models. ICDM 2017: 645-654 - [c110]Lu Hou, Quanming Yao, James T. Kwok:
Loss-aware Binarization of Deep Networks. ICLR (Poster) 2017 - [c109]Shuai Zheng, James T. Kwok:
Follow the Moving Leader in Deep Learning. ICML 2017: 4110-4119 - [c108]Quanming Yao, James T. Kwok, Fei Gao, Wei Chen, Tie-Yan Liu:
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems. IJCAI 2017: 3308-3314 - [c107]Yaqing Wang, James T. Kwok, Quanming Yao, Lionel M. Ni:
Zero-shot learning with a partial set of observed attributes. IJCNN 2017: 3777-3784 - [i15]Quanming Yao, James T. Kwok:
Accelerated and Inexact Soft-Impute for Large-Scale Matrix and Tensor Completion. CoRR abs/1703.05487 (2017) - [i14]Yue Zhu, James T. Kwok, Zhi-Hua Zhou:
Multi-Label Learning with Global and Local Label Correlation. CoRR abs/1704.01415 (2017) - [i13]Yaqing Wang, Quanming Yao, James T. Kwok, Lionel M. Ni:
Online Convolutional Sparse Coding. CoRR abs/1706.06972 (2017) - [i12]Quanming Yao, James T. Kwok, Taifeng Wang, Tie-Yan Liu:
Large-Scale Low-Rank Matrix Learning with Nonconvex Regularizers. CoRR abs/1708.00146 (2017) - 2016
- [j54]Jinho Kim, James T. Kwok, Kazutoshi Sumiya, Byoung-Tak Zhang:
Special issue: First International Conference on Big Data and Smart Computing (BigComp2014). Data Knowl. Eng. 104: 15-16 (2016) - [c106]Lu Hou, James T. Kwok, Jacek M. Zurada:
Efficient Learning of Timeseries Shapelets. AAAI 2016: 1209-1215 - [c105]Yufeng Li, James T. Kwok, Zhi-Hua Zhou:
Towards Safe Semi-Supervised Learning for Multivariate Performance Measures. AAAI 2016: 1816-1822 - [c104]Ruiliang Zhang, Shuai Zheng, James T. Kwok:
Asynchronous Distributed Semi-Stochastic Gradient Optimization. AAAI 2016: 2323-2329 - [c103]Shuai Zheng, Ruiliang Zhang, James T. Kwok:
Fast Nonsmooth Regularized Risk Minimization with Continuation. AAAI 2016: 2393-2399 - [c102]Quanming Yao, James T. Kwok:
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. ICML 2016: 2645-2654 - [c101]Quanming Yao, James T. Kwok:
Greedy Learning of Generalized Low-Rank Models. IJCAI 2016: 2294-2300 - [c100]Shuai Zheng, James T. Kwok:
Fast-and-Light Stochastic ADMM. IJCAI 2016: 2407-2613 - [c99]Xiawei Guo, James T. Kwok:
Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering. ECML/PKDD (1) 2016: 426-442 - [i11]Shuai Zheng, Ruiliang Zhang, James Tin-Yau Kwok:
Fast Nonsmooth Regularized Risk Minimization with Continuation. CoRR abs/1602.07844 (2016) - [i10]Shuai Zheng, James T. Kwok:
Fast-and-Light Stochastic ADMM. CoRR abs/1604.07070 (2016) - [i9]Quanming Yao, James T. Kwok:
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. CoRR abs/1606.03841 (2016) - [i8]Quanming Yao, James T. Kwok:
Learning of Generalized Low-Rank Models: A Greedy Approach. CoRR abs/1607.08012 (2016) - [i7]Quanming Yao, James T. Kwok:
Fast Learning with Nonconvex L1-2 Regularization. CoRR abs/1610.09461 (2016) - [i6]Lu Hou, Quanming Yao, James T. Kwok:
Loss-aware Binarization of Deep Networks. CoRR abs/1611.01600 (2016) - 2015
- [j53]Wei Bi, James T. Kwok:
Bayes-Optimal Hierarchical Multilabel Classification. IEEE Trans. Knowl. Data Eng. 27(11): 2907-2918 (2015) - [j52]Mu Li, Wei Bi, James T. Kwok, Bao-Liang Lu:
Large-Scale Nyström Kernel Matrix Approximation Using Randomized SVD. IEEE Trans. Neural Networks Learn. Syst. 26(1): 152-164 (2015) - [j51]Kai Zhang, Liang Lan, James T. Kwok, Slobodan Vucetic, Bahram Parvin:
Scaling Up Graph-Based Semisupervised Learning via Prototype Vector Machines. IEEE Trans. Neural Networks Learn. Syst. 26(3): 444-457 (2015) - [j50]En-Liang Hu, James T. Kwok:
Scalable Nonparametric Low-Rank Kernel Learning Using Block Coordinate Descent. IEEE Trans. Neural Networks Learn. Syst. 26(9): 1927-1938 (2015) - [c98]Quanming Yao, James T. Kwok:
Colorization by Patch-Based Local Low-Rank Matrix Completion. AAAI 2015: 1959-1965 - [c97]Quanming Yao, James T. Kwok, Wenliang Zhong:
Fast Low-Rank Matrix Learning with Nonconvex Regularization. ICDM 2015: 539-548 - [c96]Quanming Yao, James T. Kwok:
Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion. IJCAI 2015: 4002-4008 - [c95]Yihai Huang, James T. Kwok:
Collaborative filtering via co-factorization of individuals and groups. IJCNN 2015: 1-8 - [c94]Kai Fan, Ziteng Wang, Jeffrey M. Beck, James T. Kwok, Katherine A. Heller:
Fast Second Order Stochastic Backpropagation for Variational Inference. NIPS 2015: 1387-1395 - [p1]James Tin-Yau Kwok, Zhi-Hua Zhou, Lei Xu:
Machine Learning. Handbook of Computational Intelligence 2015: 495-522 - [i5]Ruiliang Zhang, Shuai Zheng, James T. Kwok:
Fast Distributed Asynchronous SGD with Variance Reduction. CoRR abs/1508.01633 (2015) - [i4]Quanming Yao, James Tin-Yau Kwok, Wenliang Zhong:
Fast Low-Rank Matrix Learning with Nonconvex Regularization. CoRR abs/1512.00984 (2015) - 2014
- [j49]James T. Kwok, Liqing Zhang, Hongtao Lu:
Selected papers from the 2011 International Conference on Neural Information Processing (ICONIP 2011). Neurocomputing 129: 1-2 (2014) - [j48]Wenwu He, James T. Kwok:
Simple randomized algorithms for online learning with kernels. Neural Networks 60: 17-24 (2014) - [j47]Wei Bi, James T. Kwok:
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification. IEEE Trans. Neural Networks Learn. Syst. 25(12): 2275-2287 (2014) - [c93]Shuai Zheng, James T. Kwok:
Accurate Integration of Aerosol Predictions by Smoothing on a Manifold. AAAI 2014: 1376-1383 - [c92]Wei Bi, James T. Kwok:
Multilabel Classification with Label Correlations and Missing Labels. AAAI 2014: 1680-1686 - [c91]Wenliang Zhong, James T. Kwok:
Gradient Descent with Proximal Average for Nonconvex and Composite Regularization. AAAI 2014: 2206-2212 - [c90]Wenliang Zhong, James Tin-Yau Kwok:
Accelerated Stochastic Gradient Method for Composite Regularization. AISTATS 2014: 1086-1094 - [c89]Wenliang Zhong, James Tin-Yau Kwok:
Fast Stochastic Alternating Direction Method of Multipliers. ICML 2014: 46-54 - [c88]Ruiliang Zhang, James T. Kwok:
Asynchronous Distributed ADMM for Consensus Optimization. ICML 2014: 1701-1709 - [c87]Wei Bi, Liwei Wang, James T. Kwok, Zhuowen Tu:
Learning to Predict from Crowdsourced Data. UAI 2014: 82-91 - 2013
- [j46]Yufeng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou:
Convex and scalable weakly labeled SVMs. J. Mach. Learn. Res. 14(1): 2151-2188 (2013) - [c86]James T. Kwok:
Learning from High-Dimensional Data in Multitask/Multilabel Classification. ACPR 2013: 16-17 - [c85]Leon Wenliang Zhong, James T. Kwok:
Efficient Learning for Models with DAG-Structured Parameter Constraints. ICDM 2013: 897-906 - [c84]Kai Zhang, Vincent Wenchen Zheng, Qiaojun Wang, James Tin-Yau Kwok, Qiang Yang, Ivan Marsic:
Covariate Shift in Hilbert Space: A Solution via Sorrogate Kernels. ICML (3) 2013: 388-395 - [c83]Wei Bi, James Tin-Yau Kwok:
Efficient Multi-label Classification with Many Labels. ICML (3) 2013: 405-413 - [c82]En-Liang Hu, James T. Kwok:
Flexible Nonparametric Kernel Learning with Different Loss Functions. ICONIP (2) 2013: 116-123 - [c81]En-Liang Hu, James T. Kwok:
Efficient Kernel Learning from Side Information Using ADMM. IJCAI 2013: 1408-1414 - [c80]Wenliang Zhong, James T. Kwok:
Accurate Probability Calibration for Multiple Classifiers. IJCAI 2013: 1939-1945 - [i3]Yufeng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou:
Convex and Scalable Weakly Labeled SVMs. CoRR abs/1303.1271 (2013) - [i2]Leon Wenliang Zhong, James T. Kwok:
Fast Stochastic Alternating Direction Method of Multipliers. CoRR abs/1308.3558 (2013) - 2012
- [j45]Liqing Zhang, James Tin-Yau Kwok, Changshui Zhang:
A brief introduction to the special issue for ISNN2010. Neurocomputing 76(1): 1 (2012) - [j44]Jianhua Zhao, Philip L. H. Yu, James T. Kwok:
Bilinear Probabilistic Principal Component Analysis. IEEE Trans. Neural Networks Learn. Syst. 23(3): 492-503 (2012) - [j43]Leon Wenliang Zhong, James T. Kwok:
Efficient Sparse Modeling With Automatic Feature Grouping. IEEE Trans. Neural Networks Learn. Syst. 23(9): 1436-1447 (2012) - [c79]Wei Bi, James T. Kwok:
Hierarchical Multilabel Classification with Minimum Bayes Risk. ICDM 2012: 101-110 - [c78]Wenliang Zhong, James Tin-Yau Kwok:
Convex Multitask Learning with Flexible Task Clusters. ICML 2012 - [c77]Wei Bi, James T. Kwok:
Mandatory Leaf Node Prediction in Hierarchical Multilabel Classification. NIPS 2012: 153-161 - [i1]Wenliang Zhong, James Tin-Yau Kwok:
Convex Multitask Learning with Flexible Task Clusters. CoRR abs/1206.4601 (2012) - 2011
- [j42]Wen-Yun Yang, Bao-Liang Lu, James T. Kwok:
Incorporating cellular sorting structure for better prediction of protein subcellular locations. J. Exp. Theor. Artif. Intell. 23(1): 79-95 (2011) - [j41]Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qiang Yang:
Domain Adaptation via Transfer Component Analysis. IEEE Trans. Neural Networks 22(2): 199-210 (2011) - [j40]Shutao Li, Mingkui Tan, Ivor W. Tsang, James Tin-Yau Kwok:
A Hybrid PSO-BFGS Strategy for Global Optimization of Multimodal Functions. IEEE Trans. Syst. Man Cybern. Part B 41(4): 1003-1014 (2011) - [c76]Mu Li, Xiao-Chen Lian, James T. Kwok, Bao-Liang Lu:
Time and space efficient spectral clustering via column sampling. CVPR 2011: 2297-2304 - [c75]Wenliang Zhong, James T. Kwok:
Efficient Sparse Modeling with Automatic Feature Grouping. ICML 2011: 9-16 - [c74]Wei Bi, James T. Kwok:
MultiLabel Classification on Tree- and DAG-Structured Hierarchies. ICML 2011: 17-24 - [c73]Weike Pan, James T. Kwok:
Structured clustering with automatic kernel adaptation. IJCNN 2011: 1322-1327 - [e7]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part I. Lecture Notes in Computer Science 7062, Springer 2011, ISBN 978-3-642-24954-9 [contents] - [e6]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part II. Lecture Notes in Computer Science 7063, Springer 2011, ISBN 978-3-642-24957-0 [contents] - [e5]Bao-Liang Lu, Liqing Zhang, James T. Kwok:
Neural Information Processing - 18th International Conference, ICONIP 2011, Shanghai, China, November 13-17, 2011, Proceedings, Part III. Lecture Notes in Computer Science 7064, Springer 2011, ISBN 978-3-642-24964-8 [contents] - 2010
- [j39]Ming Zhao, Shutao Li, James Tin-Yau Kwok:
Text detection in images using sparse representation with discriminative dictionaries. Image Vis. Comput. 28(12): 1590-1599 (2010) - [j38]Yan-xia Jin, Kai Zhang, James T. Kwok, Han-chang Zhou:
Fast and accurate kernel density approximation using a divide-and-conquer approach. J. Zhejiang Univ. Sci. C 11(9): 677-689 (2010) - [j37]Kai Zhang, James T. Kwok:
Simplifying mixture models through function approximation. IEEE Trans. Neural Networks 21(4): 644-658 (2010) - [j36]Wenliang Zhong, Weike Pan, James T. Kwok, Ivor W. Tsang:
Incorporating the loss function into discriminative clustering of structured outputs. IEEE Trans. Neural Networks 21(10): 1564-1575 (2010) - [j35]Kai Zhang, James T. Kwok:
Clustered Nyström method for large scale manifold learning and dimension reduction. IEEE Trans. Neural Networks 21(10): 1576-1587 (2010) - [c72]Yufeng Li, James T. Kwok, Zhi-Hua Zhou:
Cost-Sensitive Semi-Supervised Support Vector Machine. AAAI 2010: 500-505 - [c71]Mu Li, James T. Kwok, Bao-Liang Lu:
Online multiple instance learning with no regret. CVPR 2010: 1395-1401 - [c70]Mu Li, James T. Kwok, Bao-Liang Lu:
Making Large-Scale Nyström Approximation Possible. ICML 2010: 631-638 - [c69]Chonghai Hu, James T. Kwok:
Manifold regularization for structured outputs via the joint kernel. IJCNN 2010: 1-8 - [c68]Wen-Yun Yang, James T. Kwok, Bao-Liang Lu:
Spectral and Semidefinite Relaxation of the CLUHSIC Algorithm. SDM 2010: 106-117 - [e4]Liqing Zhang, Bao-Liang Lu, James Tin-Yau Kwok:
Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part I. Lecture Notes in Computer Science 6063, Springer 2010, ISBN 978-3-642-13277-3 [contents] - [e3]Liqing Zhang, Bao-Liang Lu, James Tin-Yau Kwok:
Advances in Neural Networks - ISNN 2010, 7th International Symposium on Neural Networks, ISNN 2010, Shanghai, China, June 6-9, 2010, Proceedings, Part II. Lecture Notes in Computer Science 6064, Springer 2010, ISBN 978-3-642-13317-6 [contents]
2000 – 2009
- 2009
- [j34]Kai Zhang, James T. Kwok:
Density-Weighted Nyström Method for Computing Large Kernel Eigensystems. Neural Comput. 21(1): 121-146 (2009) - [j33]Brian Kan-Wing Mak, Tsz-Chung Lai, Ivor W. Tsang, James Tin-Yau Kwok:
Maximum Penalized Likelihood Kernel Regression for Fast Adaptation. IEEE Trans. Speech Audio Process. 17(7): 1372-1381 (2009) - [j32]Kai Zhang, Ivor W. Tsang, James T. Kwok:
Maximum Margin Clustering Made Practical. IEEE Trans. Neural Networks 20(4): 583-596 (2009) - [j31]Mingqing Hu, Yiqiang Chen, James Tin-Yau Kwok:
Building Sparse Multiple-Kernel SVM Classifiers. IEEE Trans. Neural Networks 20(5): 827-839 (2009) - [c67]Bin Zhao, James Tin-Yau Kwok, Fei Wang, Changshui Zhang:
Unsupervised Maximum Margin Feature Selection with manifold regularization. CVPR 2009: 888-895 - [c66]Bin Zhao, James Tin-Yau Kwok, Changshui Zhang:
Maximum Margin Clustering with Multivariate Loss Function. ICDM 2009: 637-646 - [c65]Xi Chen, Weike Pan, James T. Kwok, Jaime G. Carbonell:
Accelerated Gradient Method for Multi-task Sparse Learning Problem. ICDM 2009: 746-751 - [c64]Yufeng Li, James T. Kwok, Zhi-Hua Zhou:
Semi-supervised learning using label mean. ICML 2009: 633-640 - [c63]Kai Zhang, James T. Kwok, Bahram Parvin:
Prototype vector machine for large scale semi-supervised learning. ICML 2009: 1233-1240 - [c62]Sinno Jialin Pan, Ivor W. Tsang, James T. Kwok, Qiang Yang:
Domain Adaptation via Transfer Component Analysis. IJCAI 2009: 1187-1192 - [c61]Chonghai Hu, James T. Kwok, Weike Pan:
Accelerated Gradient Methods for Stochastic Optimization and Online Learning. NIPS 2009: 781-789 - [c60]Yufeng Li, James T. Kwok, Ivor W. Tsang, Zhi-Hua Zhou:
A Convex Method for Locating Regions of Interest with Multi-instance Learning. ECML/PKDD (2) 2009: 15-30 - [c59]Bin Zhao, James T. Kwok, Changshui Zhang:
Multiple Kernel Clustering. SDM 2009: 638-649 - [c58]Yufeng Li, Ivor W. Tsang, James Tin-Yau Kwok, Zhi-Hua Zhou:
Tighter and Convex Maximum Margin Clustering. AISTATS 2009: 344-351 - 2008
- [j30]Ivor Wai-Hung Tsang, András Kocsor, James Tin-Yau Kwok:
Large-Scale Maximum Margin Discriminant Analysis Using Core Vector Machines. IEEE Trans. Neural Networks 19(4): 610-624 (2008) - [j29]Xianchao Xie, Shuicheng Yan, James T. Kwok, Thomas S. Huang:
Matrix-Variate Factor Analysis and Its Applications. IEEE Trans. Neural Networks 19(10): 1821-1826 (2008) - [c57]Sinno Jialin Pan, James T. Kwok, Qiang Yang:
Transfer Learning via Dimensionality Reduction. AAAI 2008: 677-682 - [c56]Sinno Jialin Pan, Dou Shen, Qiang Yang, James T. Kwok:
Transferring Localization Models across Space. AAAI 2008: 1383-1388 - [c55]Kai Zhang, Ivor W. Tsang, James T. Kwok:
Improved Nyström low-rank approximation and error analysis. ICML 2008: 1232-1239 - [e2]Niels da Vitoria Lobo, Takis Kasparis, Fabio Roli, James Tin-Yau Kwok, Michael Georgiopoulos, Georgios C. Anagnostopoulos, Marco Loog:
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR & SPR 2008, Orlando, USA, December 4-6, 2008. Proceedings. Lecture Notes in Computer Science 5342, Springer 2008, ISBN 978-3-540-89688-3 [contents] - 2007
- [j28]Patrick C. K. Hung, Dickson K. W. Chiu, W. W. Fung, William K. Cheung, Raymond K. Wong, Samuel P. M. Choi, Eleanna Kafeza, James T. Kwok, Joshua C. C. Pun, Vivying S. Y. Cheng:
End-to-end privacy control in service outsourcing of human intensive processes: A multi-layered Web service integration approach. Inf. Syst. Frontiers 9(1): 85-101 (2007) - [j27]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Surrogate maximization/minimization algorithms and extensions. Mach. Learn. 69(1): 1-33 (2007) - [j26]Jooyoung Park, Daesung Kang, Jongho Kim, James T. Kwok, Ivor W. Tsang:
SVDD-Based Pattern Denoising. Neural Comput. 19(7): 1919-1938 (2007) - [j25]Fei Wang, Jingdong Wang, Changshui Zhang, James T. Kwok:
Face recognition using spectral features. Pattern Recognit. 40(10): 2786-2797 (2007) - [j24]James T. Kwok, Ivor Wai-Hung Tsang, Jacek M. Zurada:
A Class of Single-Class Minimax Probability Machines for Novelty Detection. IEEE Trans. Neural Networks 18(3): 778-785 (2007) - [c54]Sinno Jialin Pan, James T. Kwok, Qiang Yang, Jeffrey Junfeng Pan:
Adaptive Localization in a Dynamic WiFi Environment through Multi-view Learning. AAAI 2007: 1108-1113 - [c53]Ivor W. Tsang, András Kocsor, James T. Kwok:
Simpler core vector machines with enclosing balls. ICML 2007: 911-918 - [c52]Kai Zhang, Ivor W. Tsang, James T. Kwok:
Maximum margin clustering made practical. ICML 2007: 1119-1126 - [c51]James T. Kwok, Pak-Ming Cheung:
Marginalized Multi-Instance Kernels. IJCAI 2007: 901-906 - [c50]Ivor W. Tsang, James T. Kwok:
Ensembles of Partially Trained SVMs with Multiplicative Updates. IJCAI 2007: 1089-1094 - 2006
- [j23]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Model-based transductive learning of the kernel matrix. Mach. Learn. 63(1): 69-101 (2006) - [j22]Brian Kan-Wing Mak, Roger Wend-Huu Hsiao, Simon Ka-Lung Ho, James T. Kwok:
Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting. IEEE Trans. Speech Audio Process. 14(4): 1267-1280 (2006) - [j21]Jeffrey Junfeng Pan, James T. Kwok, Qiang Yang, Yiqiang Chen:
Multidimensional Vector Regression for Accurate and Low-Cost Location Estimation in Pervasive Computing. IEEE Trans. Knowl. Data Eng. 18(9): 1181-1193 (2006) - [j20]Ivor Wai-Hung Tsang, James Tin-Yau Kwok:
Efficient hyperkernel learning using second-order cone programming. IEEE Trans. Neural Networks 17(1): 48-58 (2006) - [j19]Ivor Wai-Hung Tsang, James Tin-Yau Kwok, Jacek M. Zurada:
Generalized Core Vector Machines. IEEE Trans. Neural Networks 17(5): 1126-1140 (2006) - [j18]Haitao Zhao, Pong Chi Yuen, James T. Kwok:
A novel incremental principal component analysis and its application for face recognition. IEEE Trans. Syst. Man Cybern. Part B 36(4): 873-886 (2006) - [c49]Kai Zhang, James T. Kwok, Ming Tang:
Accelerated Convergence Using Dynamic Mean Shift. ECCV (2) 2006: 257-268 - [c48]Ivor W. Tsang, András Kocsor, James T. Kwok:
Diversified SVM Ensembles for Large Data Sets. ECML 2006: 792-800 - [c47]Ivor W. Tsang, James T. Kwok, Brian Mak, Kai Zhang, Jeffrey Junfeng Pan:
Fast Speaker Adaption Via Maximum Penalized Likelihood Kernel Regression. ICASSP (1) 2006: 997-1000 - [c46]Jooyoung Park, Daesung Kang, James T. Kwok, Sang-Woong Lee, Bon-Woo Hwang, Seong-Whan Lee:
Facial Image Reconstruction by SVDD-Based Pattern De-noising. ICB 2006: 129-135 - [c45]Pak-Ming Cheung, James T. Kwok:
A regularization framework for multiple-instance learning. ICML 2006: 193-200 - [c44]Juan Dai, Shuicheng Yan, Xiaoou Tang, James T. Kwok:
Locally adaptive classification piloted by uncertainty. ICML 2006: 225-232 - [c43]Kai Zhang, James T. Kwok:
Block-quantized kernel matrix for fast spectral embedding. ICML 2006: 1097-1104 - [c42]Shutao Li, Chen Liao, James T. Kwok:
Gene Feature Extraction Using T-Test Statistics and Kernel Partial Least Squares. ICONIP (3) 2006: 11-20 - [c41]Shutao Li, Jinglin Peng, James T. Kwok, Jing Zhang:
Multimodal Registration using the Discrete Wavelet Frame Transform. ICPR (3) 2006: 877-880 - [c40]Ivor W. Tsang, James T. Kwok, Shutao Li:
Learning the Kernel in Mahalanobis One-Class Support Vector Machines. IJCNN 2006: 1169-1175 - [c39]Ken Chen, Bao-Liang Lu, James T. Kwok:
Efficient Classification of Multi-label and Imbalanced Data using Min-Max Modular Classifiers. IJCNN 2006: 1770-1775 - [c38]Shutao Li, Chen Liao, James T. Kwok:
Wavelet-Based Feature Extraction for Microarray Data Classification. IJCNN 2006: 5028-5033 - [c37]Ivor W. Tsang, András Kocsor, James T. Kwok:
Efficient kernel feature extraction for massive data sets. KDD 2006: 724-729 - [c36]Ivor W. Tsang, James T. Kwok:
Large-Scale Sparsified Manifold Regularization. NIPS 2006: 1401-1408 - [c35]Kai Zhang, James T. Kwok:
Simplifying Mixture Models through Function Approximation. NIPS 2006: 1577-1584 - [e1]Dit-Yan Yeung, James T. Kwok, Ana L. N. Fred, Fabio Roli, Dick de Ridder:
Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR 2006 and SPR 2006, Hong Kong, China, August 17-19, 2006, Proceedings. Lecture Notes in Computer Science 4109, Springer 2006, ISBN 3-540-37236-9 [contents] - 2005
- [j17]Ivor W. Tsang, James T. Kwok, Pak-Ming Cheung:
Core Vector Machines: Fast SVM Training on Very Large Data Sets. J. Mach. Learn. Res. 6: 363-392 (2005) - [j16]Brian Mak, James Tin-Yau Kwok, Simon Ka-Lung Ho:
Kernel Eigenvoice Speaker Adaptation. IEEE Trans. Speech Audio Process. 13(5-2): 984-992 (2005) - [c34]Patrick C. K. Hung, Dickson K. W. Chiu, W. W. Fung, William K. Cheung, Raymond K. Wong, Samuel P. M. Choi, Eleanna Kafeza, James T. Kwok, Joshua C. C. Pun, Vivying S. Y. Cheng:
Towards end-to-end privacy control in the outsourcing of marketing activities: a web service integration solution. ICEC 2005: 454-461 - [c33]Ivor W. Tsang, James Tin-Yau Kwok, Pak-Ming Cheung:
Very Large SVM Training using Core Vector Machines. AISTATS 2005: 349-356 - [c32]Kai Zhang, Ming Tang, James T. Kwok:
Applying Neighborhood Consistency for Fast Clustering and Kernel Density Estimation. CVPR (2) 2005: 1001-1007 - [c31]Kin Fung Simon Wong, Ivor W. Tsang, Victor Cheung, S.-H. Gary Chan, James T. Kwok:
Position estimation for wireless sensor networks. GLOBECOM 2005: 5 - [c30]Ivor W. Tsang, James T. Kwok, Kimo T. Lai:
Core Vector Regression for very large regression problems. ICML 2005: 912-919 - [c29]Jeffrey Junfeng Pan, James T. Kwok, Qiang Yang, Yiqiang Chen:
Accurate and Low-cost Location Estimation Using Kernels. IJCAI 2005: 1366-1371 - [c28]Jingdong Wang, James T. Kwok, Helen C. Shen, Long Quan:
Data-dependent kernels for high-dimensional data classification. IJCNN 2005: 102-107 - [c27]Jooyoung Park, Daesung Kang, Jongho Kim, James Tin-Yau Kwok, Ivor Wai-Hung Tsang:
Pattern de-noising based on support vector data description. IJCNN 2005: 949-953 - [c26]Ivor Wai-Hung Tsang, Pak-Ming Cheung, James Tin-Yau Kwok:
Kernel relevant component analysis for distance metric learning. IJCNN 2005: 954-959 - 2004
- [j15]Victor Cheng, Chun Hung Li, James T. Kwok, Chi-Kwong Li:
Dissimilarity learning for nominal data. Pattern Recognit. 37(7): 1471-1477 (2004) - [j14]James Tin-Yau Kwok, Ivor Wai-Hung Tsang:
The pre-image problem in kernel methods. IEEE Trans. Neural Networks 15(6): 1517-1525 (2004) - [j13]Shutao Li, James Tin-Yau Kwok, Ivor Wai-Hung Tsang, Yaonan Wang:
Fusing images with different focuses using support vector machines. IEEE Trans. Neural Networks 15(6): 1555-1561 (2004) - [c25]Zhihua Zhang, Kap Luk Chan, James T. Kwok, Dit-Yan Yeung:
Bayesian Inference on Principal Component Analysis Using Reversible Jump Markov Chain Monte Carlo. AAAI 2004: 372-377 - [c24]Ivor W. Tsang, James T. Kwok:
Efficient Hyperkernel Learning Using Second-Order Cone Programming. ECML 2004: 453-464 - [c23]Haitao Zhao, Pong Chi Yuen, James T. Kwok, Jingyu Yang:
Incremental PCA based face recognition. ICARCV 2004: 687-691 - [c22]Brian Mak, James T. Kwok, Simon Ka-Lung Ho:
A study of various composite kernels for kernel eigenvoice speaker adaptation. ICASSP (1) 2004: 325-328 - [c21]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model. ICML 2004 - [c20]Zhihua Zhang, Dit-Yan Yeung, James T. Kwok:
Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm. ICML 2004 - [c19]Calvin S. Chu, Ivor W. Tsang, James T. Kwok:
Scaling up support vector data description by using core-sets. IJCNN 2004 - [c18]Brian Mak, Simon Ka-Lung Ho, James T. Kwok:
Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA. INTERSPEECH 2004: 2913-2916 - 2003
- [j12]Kwok-Wai Cheung, James T. Kwok, Martin H. C. Law, Kwok Ching Tsui:
Mining customer product ratings for personalized marketing. Decis. Support Syst. 35(2): 231-243 (2003) - [j11]Shutao Li, James T. Kwok, Hailong Zhu, Yaonan Wang:
Texture classification using the support vector machines. Pattern Recognit. 36(12): 2883-2893 (2003) - [j10]James T. Kwok, Ivor W. Tsang:
Linear dependency between ε and the input noise in ε-support vector regression. IEEE Trans. Neural Networks 14(3): 544-553 (2003) - [c17]James T. Kwok, Ivor W. Tsang:
Learning with Idealized Kernels. ICML 2003: 400-407 - [c16]James T. Kwok, Ivor W. Tsang:
The Pre-Image Problem in Kernel Methods. ICML 2003: 408-415 - [c15]Zhihua Zhang, James T. Kwok, Dit-Yan Yeung:
Parametric Distance Metric Learning with Label Information. IJCAI 2003: 1450- - [c14]James T. Kwok, Brian Mak, Simon Ka-Lung Ho:
Eigenvoice Speaker Adaptation via Composite Kernel PCA. NIPS 2003: 1401-1408 - 2002
- [j9]Shutao Li, James T. Kwok, Yaonan Wang:
Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Inf. Fusion 3(1): 17-23 (2002) - [j8]Shutao Li, James T. Kwok, Yaonan Wang:
Multifocus image fusion using artificial neural networks. Pattern Recognit. Lett. 23(8): 985-997 (2002) - [c13]Shutao Li, James T. Kwok, Yaonan Wang:
Fusing Images with Multiple Focuses Using Support Vector Machines. ICANN 2002: 1287-1292 - [c12]Hailong Zhu, James T. Kwok, Liangsheng Qu:
Improving De-Noising by Coefficient De-Noising and Dyadic Wavelet Transform. ICPR (2) 2002: 273- - 2001
- [j7]Shutao Li, James T. Kwok, Yaonan Wang:
Combination of images with diverse focuses using the spatial frequency. Inf. Fusion 2(3): 169-176 (2001) - [c11]Martin H. C. Law, James Tin-Yau Kwok:
Bayesian Support Vector Regression. AISTATS 2001: 162-167 - [c10]Martin H. C. Law, James T. Kwok:
Applying the Bayesian Evidence Framework to \nu -Support Vector Regression. ECML 2001: 312-323 - [c9]James T. Kwok:
Linear Dependency between epsilon and the Input Noise in epsilon-Support Vector Regression. ICANN 2001: 405-410 - 2000
- [j6]James Tin-Yau Kwok:
The evidence framework applied to support vector machines. IEEE Trans. Neural Networks Learn. Syst. 11(5): 1162-1173 (2000) - [c8]Martin H. C. Law, James T. Kwok:
Rival Penalized Competitive Learning for Model-Based Sequence Clustering. ICPR 2000: 2195-2198
1990 – 1999
- 1999
- [j5]James Tin-Yau Kwok:
Moderating the outputs of support vector machine classifiers. IEEE Trans. Neural Networks 10(5): 1018-1031 (1999) - [c7]James Tin-Yau Kwok:
Integrating the evidence framework and the support vector machine. ESANN 1999: 177-182 - [c6]James Tin-Yau Kwok:
Moderating the outputs of support vector machine classifiers. IJCNN 1999: 943-948 - 1998
- [c5]James Tin-Yau Kwok:
Automated Text Categorization Using Support Vector Machine. ICONIP 1998: 347-351 - [c4]James Tin-Yau Kwok:
Support vector mixture for classification and regression problems. ICPR 1998: 255-258 - 1997
- [j4]James Tin-Yau Kwok, Dit-Yan Yeung:
Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Trans. Neural Networks 8(3): 630-645 (1997) - [j3]James Tin-Yau Kwok, Dit-Yan Yeung:
Objective functions for training new hidden units in constructive neural networks. IEEE Trans. Neural Networks 8(5): 1131-1148 (1997) - 1996
- [j2]James Tin-Yau Kwok, Dit-Yan Yeung:
Use of bias term in projection pursuit learning improves approximation and convergence properties. IEEE Trans. Neural Networks 7(5): 1168-1183 (1996) - [c3]James Tin-Yau Kwok, Dit-Yan Yeung:
Bayesian Regularization in Constructive Neural Networks. ICANN 1996: 557-562 - 1995
- [j1]James Tin-Yau Kwok, Dit-Yan Yeung:
Improving the approximation and convergence capabilities of projection pursuit learning. Neural Process. Lett. 2(3): 20-25 (1995) - [c2]James Tin-Yau Kwok, Dit-Yan Yeung:
Efficient cross-validation for feedforward neural networks. ICNN 1995: 2789-2794 - 1993
- [c1]James Tin-Yau Kwok, Dit-Yan Yeung:
Experimental analysis of input weight freezing in constructive neural networks. ICNN 1993: 511-516
Coauthor Index
Ivor W. Tsang
aka: Ivor Wai-Hung Tsang
aka: Ivor Wai-Hung Tsang
[j87] [j78] [j77] [c153] [c142] [i44] [j71] [j70] [i39] [i38] [j69] [j68] [c130] [i35] [c128] [c127] [c124] [p2] [i29] [i28] [j64] [j63] [c122] [c121] [i24] [j60] [c115] [c113] [i20] [i18] [j57] [c112] [c111] [c110] [c108] [c107] [i15] [i13] [i12] [c102] [c101] [i9] [i8] [i7] [i6] [c98] [c97] [c96] [i4]
Leon Wenliang Zhong
aka: Wenliang Zhong
aka: Wenliang Zhong
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