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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
- 2023
[j77]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)- [j76]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) - [j75]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) - [j74]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) - [c142]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 - [c141]Weisen Jiang, Hansi Yang, Yu Zhang, James T. Kwok:
An Adaptive Policy to Employ Sharpness-Aware Minimization. ICLR 2023 - [c140]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 - [c139]Weisen Jiang, Yu Zhang, James T. Kwok:
Effective Structured Prompting by Meta-Learning and Representative Verbalizer. ICML 2023: 15186-15199 - [c138]Lifeng Shen, James T. Kwok:
Non-autoregressive Conditional Diffusion Models for Time Series Prediction. ICML 2023: 31016-31029 - [c137]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Iterative Machine Teaching. ICML 2023: 40851-40870 - [i51]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) - [i50]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) - [i49]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) - [i48]Weisen Jiang, Hansi Yang, Yu Zhang, James T. Kwok:
An Adaptive Policy to Employ Sharpness-Aware Minimization. CoRR abs/2304.14647 (2023) - [i47]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) - [i46]Weisen Jiang, Yu Zhang, James T. Kwok:
Effective Structured Prompting by Meta-Learning and Representative Verbalizer. CoRR abs/2306.00618 (2023) - [i45]Chen Zhang, Xiaofeng Cao, Weiyang Liu, Ivor W. Tsang, James T. Kwok:
Nonparametric Iterative Machine Teaching. CoRR abs/2306.03007 (2023) - [i44]Lifeng Shen, James T. Kwok:
Non-autoregressive Conditional Diffusion Models for Time Series Prediction. CoRR abs/2306.05043 (2023) - [i43]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) - [i42]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) - [i41]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) - 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) - [c136]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 - [c135]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 - [c134]Weisen Jiang, James T. Kwok, Yu Zhang:
Subspace Learning for Effective Meta-Learning. ICML 2022: 10177-10194 - [c133]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) - [c132]Lifeng Shen, Zhongzhong Yu, Qianli Ma, James T. Kwok:
Time Series Anomaly Detection with Multiresolution Ensemble Decoding. AAAI 2021: 9567-9575 - [c131]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 - [c130]Weisen Jiang, Yu Zhang, James T. Kwok:
SEEN: Few-Shot Classification with SElf-ENsemble. IJCNN 2021: 1-8 - [c129]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 - [c128]Weisen Jiang, James T. Kwok, Yu Zhang:
Effective Meta-Regularization by Kernelized Proximal Regularization. NeurIPS 2021: 26212-26222 - [c127]Zac Wellmer, James T. Kwok:
Dropout's Dream Land: Generalization from Learned Simulators to Reality. ECML/PKDD (1) 2021: 255-270 - [c126]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) - [c125]Han Shi, Haozheng Fan, James T. Kwok:
Effective Decoding in Graph Auto-Encoder Using Triadic Closure. AAAI 2020: 906-913 - [c124]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 - [c123]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 - [c122]Lifeng Shen, Zhuocong Li, James T. Kwok:
Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network. NeurIPS 2020 - [c121]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 - [c120]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) - [c119]Lu Hou, Ruiliang Zhang, James T. Kwok:
Analysis of Quantized Models. ICLR (Poster) 2019 - [c118]Quanming Yao, James Tin-Yau Kwok, Bo Han:
Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations. ICML 2019: 7035-7044 - [c117]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 - [c116]Minsam Kim, James T. Kwok:
Dynamic Unit Surgery for Deep Neural Network Compression and Acceleration. IJCNN 2019: 1-8 - [c115]Lu Hou, Jinhua Zhu, James T. Kwok, Fei Gao, Tao Qin, Tie-Yan Liu:
Normalization Helps Training of Quantized LSTM. NeurIPS 2019: 7344-7354 - [c114]Shuai Zheng, Ziyue Huang, James T. Kwok:
Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback. NeurIPS 2019: 11446-11456 - [c113]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) - [c112]Lu Hou, James T. Kwok:
Loss-aware Weight Quantization of Deep Networks. ICLR (Poster) 2018 - [c111]Yaqing Wang, Quanming Yao, James Tin-Yau Kwok, Lionel M. Ni:
Online Convolutional Sparse Coding with Sample-Dependent Dictionary. ICML 2018: 5196-5205 - [c110]Shuai Zheng, James Tin-Yau Kwok:
Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data. ICML 2018: 5927-5935 - [c109]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) - [c108]Xiawei Guo, Quanming Yao, James Tin-Yau Kwok:
Efficient Sparse Low-Rank Tensor Completion Using the Frank-Wolfe Algorithm. AAAI 2017: 1948-1954 - [c107]Huan Zhao, Quanming Yao, James T. Kwok, Dik Lun Lee:
Collaborative Filtering with Social Local Models. ICDM 2017: 645-654 - [c106]Lu Hou, Quanming Yao, James T. Kwok:
Loss-aware Binarization of Deep Networks. ICLR (Poster) 2017 - [c105]Shuai Zheng, James T. Kwok:
Follow the Moving Leader in Deep Learning. ICML 2017: 4110-4119 - [c104]Quanming Yao, James T. Kwok, Fei Gao, Wei Chen, Tie-Yan Liu:
Efficient Inexact Proximal Gradient Algorithm for Nonconvex Problems. IJCAI 2017: 3308-3314 - [c103]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) - [c102]Lu Hou, James T. Kwok, Jacek M. Zurada:
Efficient Learning of Timeseries Shapelets. AAAI 2016: 1209-1215 - [c101]Yu-Feng Li, James T. Kwok, Zhi-Hua Zhou:
Towards Safe Semi-Supervised Learning for Multivariate Performance Measures. AAAI 2016: 1816-1822 - [c100]Ruiliang Zhang, Shuai Zheng, James T. Kwok:
Asynchronous Distributed Semi-Stochastic Gradient Optimization. AAAI 2016: 2323-2329 - [c99]Shuai Zheng, Ruiliang Zhang, James T. Kwok:
Fast Nonsmooth Regularized Risk Minimization with Continuation. AAAI 2016: 2393-2399 - [c98]Quanming Yao, James T. Kwok:
Efficient Learning with a Family of Nonconvex Regularizers by Redistributing Nonconvexity. ICML 2016: 2645-2654 - [c97]Quanming Yao, James T. Kwok:
Greedy Learning of Generalized Low-Rank Models. IJCAI 2016: 2294-2300 - [c96]Shuai Zheng, James T. Kwok:
Fast-and-Light Stochastic ADMM. IJCAI 2016: 2407-2613 - [c95]Xiawei Guo, James T. Kwok:
Aggregating Crowdsourced Ordinal Labels via Bayesian Clustering. ECML/PKDD (1) 2016: 426-442 - [i11]