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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
- 2022
[j70]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)- [i37]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) - [i36]Quanming Yao, Yaqing Wang, Bo Han, James T. Kwok:
Low-rank Tensor Learning with Nonconvex Overlapped Nuclear Norm Regularization. CoRR abs/2205.03059 (2022) - 2021
- [j69]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) - [j68]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) - [j67]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) - [c131]Lifeng Shen, Zhongzhong Yu, Qianli Ma, James T. Kwok:
Time Series Anomaly Detection with Multiresolution Ensemble Decoding. AAAI 2021: 9567-9575 - [c130]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 - [c129]Weisen Jiang, Yu Zhang, James T. Kwok:
SEEN: Few-Shot Classification with SElf-ENsemble. IJCNN 2021: 1-8 - [c128]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 - [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
- [j66]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 (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]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) - [c94]Quanming Yao, James T. Kwok:
Colorization by Patch-Based Local Low-Rank Matrix Completion. AAAI 2015: 1959-1965 - [c93]Quanming Yao, James T. Kwok, Wenliang Zhong:
Fast Low-Rank Matrix Learning with Nonconvex Regularization. ICDM 2015: 539-548 - [c92]Quanming Yao, James T. Kwok:
Accelerated Inexact Soft-Impute for Fast Large-Scale Matrix Completion. IJCAI 2015: 4002-4008 - [c91]Yihai Huang, James T. Kwok:
Collaborative filtering via co-factorization of individuals and groups. IJCNN 2015: 1-8 - [c90]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) - [c89]Shuai Zheng, James T. Kwok:
Accurate Integration of Aerosol Predictions by Smoothing on a Manifold. AAAI 2014: 1376-1383 - [c88]Wei Bi, James T. Kwok:
Multilabel Classification with Label Correlations and Missing Labels. AAAI 2014: 1680-1686 - [c87]Wenliang Zhong, James T. Kwok:
Gradient Descent with Proximal Average for Nonconvex and Composite Regularization. AAAI 2014: 2206-2212 - [c86]Wenliang Zhong, James Tin-Yau Kwok:
Accelerated Stochastic Gradient Method for Composite Regularization. AISTATS 2014: 1086-1094 - [c85]Wenliang Zhong, James Tin-Yau Kwok:
Fast Stochastic Alternating Direction Method of Multipliers. ICML 2014: 46-54 - [c84]Ruiliang Zhang, James T. Kwok:
Asynchronous Distributed ADMM for Consensus Optimization. ICML 2014: 1701-1709 - [c83]Wei Bi, Liwei Wang, James T. Kwok, Zhuowen Tu:
Learning to Predict from Crowdsourced Data. UAI 2014: 82-91 - 2013
- [j46]Yu-Feng 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) - [c82]James T. Kwok:
Learning from High-Dimensional Data in Multitask/Multilabel Classification. ACPR 2013: 16-17 - [c81]Leon Wenliang Zhong, James T. Kwok:
Efficient Learning for Models with DAG-Structured Parameter Constraints. ICDM 2013: 897-906 - [c80]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 - [c79]Wei Bi, James Tin-Yau Kwok:
Efficient Multi-label Classification with Many Labels. ICML (3) 2013: 405-413 - [c78]En-Liang Hu, James T. Kwok:
Flexible Nonparametric Kernel Learning with Different Loss Functions. ICONIP (2) 2013: 116-123 - [c77]En-Liang Hu, James T. Kwok:
Efficient Kernel Learning from Side Information Using ADMM. IJCAI 2013: 1408-1414 - [c76]Wenliang Zhong, James T. Kwok:
Accurate Probability Calibration for Multiple Classifiers. IJCAI 2013: 1939-1945 - [i3]Yu-Feng Li, Ivor W. Tsang, James T. Kwok, Zhi-Hua Zhou:
Convex and Scalable Weakly Labeled SVMs. CoRR abs/1303.1271 (2013) - [i2]