Publications

Publications

[ Conference/Journal Papers | Edited Books ]

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Conference / Journal Papers

    2024

  1. L. Shen, W. Chen, J. Kwok. Multi-resolution diffusion models for time series forecasting. To appear in International Conference on Learning Representations (ICLR), 2024.

  2. L. Yu, W. Jiang, H. Shi, J. Yu, Z. Liu, Y. Zhang, J. Kwok, Z. Li, A. Weller, W. Liu. MetaMath: Bootstrap your own mathematical questions for large language models. To appear in International Conference on Learning Representations (ICLR), 2024.

  3. J. Chen, J. Yu, C. Ge, L. Yao, E. Xie, Z. Wang, J. Kwok, P. Luo, H. Lu, Z. Li. PixArt-alpha: Fast training of diffusion transformer for photorealistic text-to-image synthesis. To appear in International Conference on Learning Representations (ICLR), 2024.

    2023

  4. Y. Wei, Q. Huang, Y. Zhang, J.T.Kwok. KICGPT: Large Language Model with Knowledge in Context for Knowledge Graph Completion. Findings of EMNLP, 2023.

  5. Z. Shen, H. Yang, Y. Li, J. Kwok, and Q. Yao. Efficient hyper-parameter optimization with cubic regularization. Neural Information Processing Systems (NeurIPS), 2023.

  6. C. Zhang, X. Cao, W. Liu, I.W. Tsang, J.T. Kwok. Nonparametric teaching for multiple learners. Neural Information Processing Systems (NeurIPS), 2023.

  7. H. Yang, Y. Zhang, Q. Yao and J. Kwok. Positive-unlabeled node classification with structure-aware graph learning. ACM International Conference on Information and Knowledge Management (CIKM), 2023.

  8. X. Deng, H. Shi, R. Huang, C. Li, H. Xu, J. Han, J. Kwok, S. Zhao, W. Zhang and X. Liang. GrowCLIP: Data-aware automatic model growing for large-scale contrastive language-image pre-training. International Conference on Computer Vision (ICCV), 2023.

  9. L. Shen, J. Kwok. Non-autoregressive conditional diffusion models for time series prediction. International Conference on Machine Learning (ICML), 2023.

  10. W. Jiang, Y. Zhang, J. Kwok. Effective structured-prompting by meta-learning and representitive verbalizer. International Conference on Machine Learning (ICML), 2023.

  11. C. Zhang, X. Cao, W. Liu, I. Tsang, J. Kwok. Nonparametric iterative machine teaching. International Conference on Machine Learning (ICML), 2023.

  12. Y. Gou, T. Ko, H. Yang, J. Kwok, Y. Zhang, M. Wang. Leveraging per image-token consistency for vision-language pre-training. IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR), 2023.

  13. Y. Guo, X. Liang, J.T. Kwok, X. Zheng, B. Wu, Y. Ma. Cross-modal matching and adaptive graph attention network for RGB-D scene recognition. International Conference on Acoustics, Speech, and Signal Processing Conference (ICASSP), 2023.

  14. Z. Liu, K. Chen, J. Han, L. Hong, H. Xu, Z. Li, J. Kwok. Task-customized masked autoencoder via mixture of cluster-conditional experts. International Conference on Learning Representations (ICLR), 2023.

  15. R. Yu, W. Chen, X. Wang, J. Kwok. Enhancing meta learning via multi-objective soft improvement functions. International Conference on Learning Representations (ICLR), 2023.

  16. W. Jiang, H. Yang, Y. Zhang, J. Kwok. An adaptive policy to employ sharpness-aware minimization. International Conference on Learning Representations (ICLR), 2023.

  17. H. Zhang, Q. Yao, J.T. Kwok, X. Bai. Searching a high performance feature extractor for text recognition network. To appear in IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023.

  18. F. Feng, L. Hou, S. Qi, R. Chan, J. Kwok. Power law in deep neural networks: Sparse network generation and continual learning with preferential attachment. To appear in IEEE Transactions on Neural Networks and Learning Systems, 2023.

  19. B. Cao, J. Cao, J. Gui, J. Shen, B. Liu, L. He, Y.Y. Tang, J.T. Kwok. AlignVE: Visual entailment recognition based on alignment relations. IEEE Transactions on Multimedia, 25:7378-7387, 2023.

  20. Y. Zhang, Q. Yao, J. Kwok. Bilinear scoring function search for knowledge graph learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, 45(2): 1458-1473, 2023.

    2022

  21. W. Chen, J. Kwok. Multi-objective deep learning with adaptive reference vectors. Neural Information Processing Systems (NeurIPS), 2022.

  22. S. Li, F. Lv, T. Jin, G. Li, Y. Zheng, T. Zhuang, Q. Liu, X. Zeng, J. Kwok, Q. Ma. Query rewriting in TaoBao search. International Conference on Information Knowledge Management (CIKM), 2022.

  23. H. Yang, J. Kwok. Efficient variance reduction for meta-learning. International Conference on Machine Learning (ICML), 2022.

  24. W. Jiang, J. Kwok, Y. Zhang. Subspace learning for effective meta-learning. International Conference on Machine Learning (ICML), 2022.

  25. H. Shi, J. Gao, H. Xu, X. Liang, Z. Li, L. Kong, S. Lee, J.T. Kwok. Revisiting over-smoothing in BERT from the perspective of graph. International Conference on Learning Representations (ICLR), 2022.

  26. Q. Yao, Y. Wang, B. Han, J.T. Kwok. Low-rank tensor learning with nonconvex overlapped nuclear norm regularization. Journal of Machine Learning Research, 23:1-60, 2022.

  27. Q. Yao, H. Yang, E.-L. Hu, J.T. Kwok. Efficient low-rank semidefinite programming with robust loss functions. IEEE Transactions on Pattern Analysis and Machine Intelligence, 44(10), 6153-6168, October 2022.

    2021

  28. W. Jiang, J.T. Kwok, Y. Zhang. Effective meta-regularization by kernelized proximal regularization. Neural Information Processing Systems (NeurIPS), December 2021.

  29. H. Chi, F. Liu, W. Yang, L. Lan, T. Liu, B. Han, W. Cheung, J.T. Kwok. TOHAN: A one-step approach towards few-shot hypothesis adaptation. Neural Information Processing Systems (NeurIPS), December 2021.

  30. Z. Wellmer, J.T. Kwok. Dropout's dream land: Generalization from learned simulators to reality. European Conference on Machine Learning (ECML), 2021.

  31. H. Shi, J. Gao, X. Ren, H. Xu, X. Liang, Z. Li, J.T. Kwok. SparseBERT: Rethinking the importance analysis in self-attention. International Conference on Machine Learning (ICML), 2021.

  32. W. Jiang, Y. Zhang, J.T. Kwok. SEEN: Few-Shot Classification with SElf-ENsemble. International Joint Conference on Neural Networks (IJCNN), 2021.

  33. Y. Wang, Q. Yao, J.T. Kwok. A scalable, adaptive and sound nonconvex regularizer for low-rank matrix completion. The Web Conference, 2021.

  34. L. Shen, Z. Yu, Q. Ma, J.T. Kwok. Time series anomaly detection with multiresolution ensemble decoding. Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI), 2021.

  35. H. Zhao, Q. Yao, Y. Song, J.T. Kwok, D.L. Lee. Side information fusion for recommender systems over heterogeneous information network. ACM Transactions on Knowledge Discovery from Data, 15(4), April 2021.

  36. Y. Ma, X. Liang, G. Sheng, J.T. Kwok, M. Wang, G. Li. Non-iterative sparse LS-SVM based on globally representative point selection. IEEE Transactions on Neural Networks and Learning Systems, 32(2):788-798, Feb 2021.

    2020

  37. L. Shen, Z. Li, J.T. Kwok. Timeseries anomaly detection using temporal hierarchical one-class network. Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2020.

  38. H. Shi, R. Pi, H. Xu, Z. Li, J.T. Kwok, T. Zhang. Bridging the gap between sample-based and one-shot neural architecture search with BONAS. Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2020

  39. Q. Yao, H. Yang, B. Han, G. Niu, J.T. Kwok. Searching to exploit memorization effect in learning from noisy labels. International Conference on Machine Learning (ICML), July 2020.

  40. Q. Yao, X. Chen, J.T. Kwok, Y. Li, C.-J.Hsieh. Efficient one-shot interaction functions search for collaborative filtering. The Web Conference, Taipei, Taiwan, April 2020.

  41. H. Shi, H. Fan, J.T. Kwok. Effective decoding in graph auto-encoder using triadic closure. Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI), New York City, New York, USA, February 2020.

  42. Y. Cao, H. Qi, J. Gui, K. Li, Y.Y. Tang, J.T. Kwok. Learning to hash with a dimension analysis-based quantizer for image retrieval. IEEE Transactions on Multimedia, 2020.

  43. Y. Wang, Q. Yao, J.T. Kwok, L.M. Ni. Generalizing from a few examples: A survey on few-shot learning. ACM Computing Surveys, 53(3):63:1-34, Jun 2020.

  44. Y. Wang, J.T. Kwok, L.M. Ni. Generalized convolutional sparse coding with unknown noise. IEEE Transactions on Image Processing, 29:5386-5395, Mar 2020.

    2019

  45. S. Zheng, Z. Huang, J.T. Kwok. Communication-efficient distributed blockwise momentum SGD with error-feedback. Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019.

  46. L. Hou, J. Zhu, J.T. Kwok. F. Gao, T. Qin, T.-Y. Liu. Normalization helps training of quantized LSTM. Neural Information Processing Systems (NeurIPS), Vancouver, Canada, December 2019.

  47. Z. Wellmer, J.T. Kwok. Policy prediction network: Model-free behavior policy with model-based learning in continuous action space. European Conference on Machine Learning (ECML), Wurzburg, Germany, Sept 2019.

  48. Q. Yao, X. Guo, J.T. Kwok, W. Tu, Y. Chen, D. Wen, Q. Yang. Privacy-preserving stacking with application to cross-organizational diabetes prediction. International Joint Conference on Artificial Intelligence (IJCAI), Macao, China, Aug 2019.

  49. M. Kim, J.T. Kwok. Dynamic unit surgery for deep neural network compression and acceleration. International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, Jul 2019.

  50. Q. Yao, J.T. Kwok, H. Bo. Efficient nonconvex regularized tensor completion with structure-aware proximal iterations. International Conference on Machine Learning (ICML), Long Beach, California, USA, Jun 2019.

  51. L. Hou, R. Zhang, J.T. Kwok. Analysis of quantized models. International Conference on Learning Representations (ICLR), New Orleans, Louisinna, May 2019.

  52. E.-L. Hu, J.T. Kwok. Low-rank matrix learning using biconvex surrogate minimization. IEEE Transactions on Neural Networks and Learning Systems, 30(11):3517-3527, Nov 2019.

  53. Q. Yao, J.T. Kwok. Accelerated and inexact soft-impute for large-scale matrix and tensor completion. IEEE Transactions on Knowledge and Data Engineering, 31(9):1665-1679, Sept 2019.

  54. Q. Yao, J.T. Kwok, T.Wang, T.-Y. Liu. Large-scale low-rank matrix learning with nonconvex regularizers. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(11):2628-2643, Nov 2019.

    2018

  55. Q. Yao, J.T. Kwok. Scalable robust matrix factorization with nonconvex loss. Neural Information Processing Systems (NeurIPS), Montreal, Canada, December 2018.

  56. S. Zheng, J.T. Kwok. Lightweight stochastic optimization for minimizing finite sums with infinite data. International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018.

  57. Y. Wang, Q. Yao, J.T. Kwok, L.M. Ni. Online convolutional sparse coding with sample-dependent dictionary. International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018.

  58. L. Hou, J.T. Kwok. Loss-aware weight quantization of deep networks. International Conference on Learning Representations (ICLR), Vancouver, BC, Canada, April 2018.

  59. Y. Wang, Q. Yao, J.T. Kwok, L. Ni. Scalable online convolutional sparse coding. IEEE Transactions on Image Processing, 27(10): 4850-4859, Oct 2018.

  60. Q. Yao, J.T. Kwok. Efficient learning with nonconvex regularizers by nonconvexity redistribution. Journal of Machine Learning Research, 18(179):1-52, 2018.

  61. Y. Zhu, J.T. Kwok, Z.-H. Zhou. Multi-label learning with global and local label correlation. IEEE Transactions on Knowledge and Data Engineering, 30(6):1081-1094, Jun 2018.

  62. Y. Ma, X. Liang, J.T. Kwok. J. Li, X. Zhou, H. Zhang. Fast-solving quasi-optimal LS-S3VM based on an extended candidate set. IEEE Transactions on Neural Networks and Learning Systems, 29(4): 1120-1131, April 2018.

    2017

  63. H. Zhao, Q. Yao, J.T. Kwok, D.L. Lee. Collaborative filtering with social local models. International Conference on Data Mining (ICDM), New Orleans, USA, November 2017.

  64. Q. Yao, J.T. Kwok, F. Gao, W. Chen, T.-Y. Liu. Efficient inexact proximal gradient algorithm for nonconvex problems. International Joint Conference on Artificial Intelligence (IJCAI), Melbourne, Australia, August 2017.

  65. S. Zheng, J.T. Kwok. Follow the moving leader in deep learning. International Conference on Machine Learning (ICML), Sydney, Australia, August 2017.

  66. Y. Wang, J.T. Kwok, Q. Yao, L.M. Ni. Zero-shot learning with a partial set of observed attributes. International Joint Conference on Neural Networks (IJCNN), Anchorage, Alaska, USA, May 2017.

  67. L. Hou, Q. Yao, J.T. Kwok. Loss-aware binarization of deep networks. International Conference on Learning Representations (ICLR), Toulon, France, Apr 2017.

  68. X. Guo, Q. Yao, J.T. Kwok. Efficient sparse low-rank tensor completion using Frank-Wolfe algorithm. Thirty-First AAAI Conference on Artificial Intelligence (AAAI), San Francisco, California USA, Feb 2017.

  69. W. He, J.T. Kwok, J. Zhu, Y. Liu. A note on the unification of adaptive online learning. IEEE Transactions on Neural Networks and Learning Systems, 28(5): 1178-1191, February 2017.

    2016

  70. X. Guo, J.T. Kwok. Aggregating crowdsourced ordinal labels via Bayesian clustering. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery (ECML-PKDD), Riva del Garda, Italy, Sept 2016.

  71. S. Zheng, J.T. Kwok. Fast-and-light stochastic ADMM. International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, USA, Jul 2016.

  72. Q. Yao, J.T. Kwok. Greedy learning of generalized low-rank models. International Joint Conference on Artificial Intelligence (IJCAI), New York, NY, USA, Jul 2016.

  73. Q. Yao, J.T. Kwok. Efficient learning with a family of nonconvex regularizers by redistributing nonconvexity. International Conference on Machine Learning (ICML), New York, NY, USA, Jun 2016.

  74. S. Zheng, R. Zhang, J.T. Kwok. Fast nonsmooth regularized risk minimization with continuation. Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp.2393-2399, Phoenix, AZ, USA, Feb 2016.

  75. R. Zhang, S. Zheng, J.T. Kwok. Asynchronous distributed semi-stochastic gradient optimization. Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp.2323-2329, Phoenix, AZ, USA, Feb 2016.

  76. L. Hou, J.T. Kwok, J.M. Zurada. Efficient learning of timeseries shapelets. Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp.1209-1215, Phoenix, AZ, USA, Feb 2016.

  77. Y.-F. Li, J.T. Kwok, Z.-H. Zhou. Towards safe semi-supervised learning for multivariate performance measures. Thirtieth AAAI Conference on Artificial Intelligence (AAAI), pp.1816-1822, Phoenix, AZ, USA, Feb 2016.

    2015

  78. K. Fan, Z. Wang, J. Beck, J.T. Kwok, K. Heller. Fast second order stochastic backpropagation for variational inference. Neural Information Processing Systems (NIPS), Montreal, Canada, Dec 2015.

  79. Q. Yao, J.T. Kwok, L.W. Zhong. Fast low-rank matrix learning with nonconvex regularization. International Conference on Data Mining (ICDM), Atlantic City, NJ, USA, Nov 2015.

  80. Q. Yao, J.T. Kwok. Accelerated inexact soft-impute for fast large-scale matrix completion. International Joint Conference on Artificial Intelligence (IJCAI), Buenos Aires, Argentina, Jul 2015.

  81. Y. Huang, J.T. Kwok. Collaborative filtering via co-factorization of individuals and groups. International Joint Conference on Neural Networks (IJCNN), Killarney, Ireland, UK, Jul 2015.

  82. Q. Yao, J.T. Kwok. Colorization by patch-based local low-rank matrix completion. Twenty-Ninth AAAI Conference on Artificial Intelligence (AAAI), Austin, Texas, USA, Jan 2015.

  83. W. Bi, J.T. Kwok. Bayes-optimal hierarchical multilabel classification. IEEE Transactions on Knowledge and Data Engineering, 27(11): 2907-2918, November 2015.

  84. E.-L. Hu, J.T. Kwok. Scalable nonparametric low-rank kernel learning using block coordinate descent. IEEE Transactions on Neural Networks and Learning Systems, 26(9): 1927-1938, 2015.

  85. K. Zhang, L. Lan, J.T. Kwok, S. Vucetic, B. Parvin. Scaling up graph-based semi-supervised learning via prototype vector machines. IEEE Transactions on Neural Networks and Learning Systems, 26(3): 444-457, 2015.

  86. M. Li, W. Bi, J.T. Kwok, B. Lu. Large-scale Nystrom kernel matrix approximation using randomized SVD. IEEE Transactions on Neural Networks and Learning Systems, 26(1); 152-164, 2015.

    2014

  87. S. Zheng, J.T. Kwok. Accurate integration of aerosol predictions by smoothing on a manifold. Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), Quebec City, Canada, July 2014.

  88. L.W. Zhong, J.T. Kwok. Gradient descent method with proximal average for nonconvex and composite regularization. Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), Quebec City, Canada, July 2014.

  89. W. Bi, J.T. Kwok. Multilabel classification with label correlations and missing labels. Twenty-Eighth AAAI Conference on Artificial Intelligence (AAAI), Quebec City, Canada, July 2014.

  90. W. Bi, L. Wang, J.T. Kwok, Z. Tu. Learning to predict from crowdsourced data. Conference on Uncertainty in Artificial Intelligence (UAI), Quebec City, Canada, July 2014.

  91. R. Zhang, J.T. Kwok. Asynchronous distributed ADMM for consensus optimization. International Conference on Machine Learning (ICML), Beijing, China, June 2014.

  92. L.W. Zhong, J.T. Kwok. Fast stochastic alternating direction method of multipliers. International Conference on Machine Learning (ICML), Beijing, China, June 2014.

  93. L.W. Zhong, J.T. Kwok. Accelerated stochastic gradient method for composite regularization. International Conference on Artificial Intelligence and Statistics (AISTATS), Reykjavik, Iceland, April 2014.

  94. W. He, J.T. Kwok. Simple randomized algorithms for online learning with kernels. Neural Networks, 60: 17-24, 2014.

  95. W. Bi, J.T. Kwok. Mandatory leaf node prediction in hierarchical multilabel classification. IEEE Transactions on Neural Networks and Learning Systems, 25(12): 2275-2287, 2014.

    2013

  96. L.W. Zhong, J.T. Kwok. Efficient learning for models with DAG-structured parameter constraints. International Conference on Data Mining (ICDM), Dallas, Texas, USA, December 2013.

  97. E. Hu, J.T. Kwok. Flexible nonparametric kernel learning with different loss functions. International Conference on Neural Information Processing (ICONIP), Daegu, Korea, Nov 2013.

  98. L.W. Zhong, J.T. Kwok. Accurate probability calibration for multiple classifiers. International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, July 2013.

  99. E.-L. Hu, J.T. Kwok. Efficient kernel learning from side information using ADMM. International Joint Conference on Artificial Intelligence (IJCAI), Beijing, China, July 2013.

  100. W. Bi, J.T. Kwok. Efficient multi-label classification with many labels. International Conference on Machine Learning (ICML), Atlanta, Georgia, USA, June 2013.

  101. K. Zhang, V.W. Zheng, Q. Wang, J.T. Kwok, Q. Yang, I. Marsic. Covariate shift in Hilbert space: A solution via surrogate kernels. International Conference on Machine Learning (ICML), Atlanta, Georgia, USA, June 2013.

  102. Y.-F. Li, I.W. Tsang, J.T. Kwok, Z.-H. Zhou. Convex and scalable weakly labeled SVMs. Journal of Machine Learning Research, 14(Jul):2151-2188, 2013.

    2012

  103. W. Bi, J.T. Kwok. Hierarchical multilabel classification with minimum Bayes risk. International Conference on Data Mining (ICDM), Brussels, Belgium, December 2012.

  104. W. Bi, J.T. Kwok. Mandatory leaf node prediction in hierarchical multilabel classification. Neural Information Processing Systems (NIPS), Lake Tahoe, CA, USA, December 2012.

  105. L.W. Zhong, J.T. Kwok. Convex multitask learning with flexible task clusters. Twenty-Nineth International Conference on Machine Learning (ICML), Edinburgh, Scotland, June 2012.

  106. L.W. Zhong, J.T. Kwok. Efficient sparse modeling with automatic feature grouping. IEEE Transactions on Neural Networks and Learning Systems, 23(9): 1436-1447, September 2012.

  107. J. Zhao, P.L.H. Yu, J.T. Kwok. Bilinear probabilistic principal component analysis. IEEE Transactions on Neural Networks and Learning Systems (formerly called IEEE Transactions on Neural Networks), 23(3): 492-503, March 2012.

    2011

  108. W. Pan, J.T. Kwok. Structured clustering with automatic kernel adaptation. International Joint Conference on Neural Networks (IJCNN), San Jose, CA, USA, July 2011.

  109. L.W. Zhong, J.T. Kwok. Efficient sparse modeling with automatic feature grouping. Twenty-Eighth International Conference on Machine Learning (ICML), Bellevue, WA, USA, June 2011.

  110. W. Bi, J.T. Kwok. Multi-label classification on tree- and DAG-structured hierarchies. Twenty-Eighth International Conference on Machine Learning (ICML), Bellevue, WA, USA, June 2011.

  111. M. Li, X.-C. Lian, J.T. Kwok, B. Lu. Time and space efficient spectral clustering via column sampling. International Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, USA, June 2011.

  112. S. Li, M. Tan, I.W. Tsang, J.T. Kwok. A hybrid PSO-BFGS strategy for global optimization of multimodal functions, IEEE Transactions on Systems, Man and Cybernetics (Part B), 41(4):1003-1014, August 2011.

  113. S.J. Pan, I.W. Tsang, J.T. Kwok, Q. Yang. Domain adaptation via transfer component analysis. IEEE Transactions on Neural Networks. 22(2): 199-210, Feb 2011.

    2010

  114. Y.-F. Li, J.T. Kwok, Z.-H. Zhou. Cost-sensitive semi-supervised support vector machine. Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI), Atlanta, Georgia, USA. July 2010.

  115. C. Hu, J.T. Kwok. Manifold regularization for structured outputs via the joint kernel. International Joint Conference on Neural Networks (IJCNN), Barcelona, Spain, July 2010.

  116. M. Li, J.T. Kwok, B. Lu. Making large-scale Nystrom approximation possible. Twenty-Seventh International Conference on Machine Learning (ICML), Haifa, Isreal, June 2010.

  117. M. Li, J.T. Kwok, B. Lu. Online multiple instance learning with no regret. International Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA, June 2010.

  118. W. Yang, J.T. Kwok, B. Lu. Spectral and semidefinite relaxations of the CLUHSIC algorithm. SIAM International Conference on Data Mining (SDM), Columbus, Ohio, USA, Apr 2010.

  119. M. Zhao, S. Li, J.T. Kwok. Text detection in images using sparse representation with discriminative dictionaries. Image and Vision Computing. 28(12): 1590-1599, Dec 2010.

  120. K. Zhang, J.T. Kwok. Clustered Nystrom method for large scale manifold learning and dimension reduction. IEEE Transactions on Neural Networks. 21(10): 1576-1587, Oct 2010.

  121. W. Zhong, W. Pan, J.T. Kwok, I.W. Tsang. Incorporating the loss function into discriminative clustering of structured outputs. IEEE Transactions on Neural Networks. 21(10): 1564-1575, Oct 2010.

  122. K. Zhang, J.T. Kwok. Simplifying mixture models through function approximation. IEEE Transactions on Neural Networks. 21(4): 644-658, April 2010.

    2009

  123. C. Hu, J.T. Kwok, W. Pan. Accelerated gradient methods for stochastic optimization and online learning. Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2009.

  124. X. Chen, W. Pan, J.T. Kwok, J. Carbonell. Accelerated gradient method for multi-task sparse learning problem. International Conference on Data Mining (ICDM), Miami, Florida, USA, December 2009.

  125. B. Zhao, J.T. Kwok, C. Zhang. Maximum margin clustering with multivariate loss function. International Conference on Data Mining (ICDM), Miami, Florida, USA, December 2009.

  126. Y.-F. Li, J.T. Kwok, I.W. Tsang, Z.-H. Zhou. A convex method for locating regions of interest with multi-instance learning. European Conference on Machine Learning (ECML), Bled, Slovenia, September 2009.

  127. S.J. Pan, I.W. Tsang, J.T. Kwok, Q. Yang. Domain adaptation via transfer component analysis. Twenty-First International Joint Conference on Artificial Intelligence (IJCAI), Pasadena, California, USA, July 2009.

  128. B. Zhao, J.T. Kwok, F. Wang, C. Zhang. Unsupervised maximum margin feature selection with manifold regularization. International Conference on Computer Vision and Pattern Recognition (CVPR), Miami, FL, USA, June 2009.

  129. K. Zhang, J.T. Kwok, B. Parvin. Prototype vector machine for large scale semi-supervised learning. Twenty-Sixth International Conference on Machine Learning (ICML), Montreal, Canada. June 2009.

  130. Y.-F. Li, J.T. Kwok, Z.-H. Zhou. Semi-supervised learning using label mean. Twenty-Sixth International Conference on Machine Learning (ICML), Montreal, Canada. June 2009.

  131. B. Zhao, J.T. Kwok, C. Zhang. Multiple kernel clustering. SIAM International Conference on Data Mining (SDM). Sparks, Nevada, April 2009.

  132. Y.-F. Li, I.W. Tsang, J.T. Kwok, Z.-H. Zhou. Tighter and convex maximum margin clustering. Twelfth International Conference on Artificial Intelligence and Statistics (AISTATS). Clearwater Beach, Florida, USA, April 2009.

  133. B. Mak, T.-C. Lai, I.W. Tsang, J.T. Kwok. Maximum penalized likelihood kernel regression for fast adaptation. IEEE Transactions on Audio, Speech and Language Processing. 17(7): 1372-1381, September 2009.

  134. M. Hu, Y. Chen, J.T. Kwok. Building sparse multi-kernel SVM classifiers. IEEE Transactions on Neural Networks, 20(5): 827-839, May 2009.

  135. K. Zhang, I.W. Tsang, J.T. Kwok. Maximum margin clustering made practical. IEEE Transactions on Neural Networks. 20(4): 583-596, Apr 2009.

  136. K. Zhang, J.T. Kwok. Density-weighted Nystrom method for computing large kernel eigen-systems. Neural Computation. 21(1): 121-146, Jan 2009.

    2008

  137. S.J. Pan, J.T. Kwok, Q. Yang. Transfer learning via dimensionality reduction. Twenty-Third AAAI Conference on Artificial Intelligence (AAAI), Chicago, Illinois, USA. July 2008.

  138. S.J. Pan, D. Shen, Q. Yang, J.T. Kwok. Transferring localization models across space. the Twenty-Third AAAI Conference on Artificial Intelligence (AAAI), Chicago, Illinois, USA. July 2008.

  139. K. Zhang, I.W. Tsang, J.T. Kwok. Improved Nystrom low rank approximation and error analysis. Twenty-Fifth International Conference on Machine Learning (ICML), Helsinki, Finland. July 2008.

  140. X. Xie, S. Yan, J. Kwok, and T.S. Huang. Matrix-variate factor analysis and its applications. IEEE Transactions on Neural Networks. 19(10): 1821-1826, Oct 2008.

  141. I.W. Tsang, A. Kocsor, J.T. Kwok. Large-scale maximum margin discriminant analysis using core vector machines. IEEE Transactions on Neural Networks. 19(4): 610-624, Apr 2008.

  142. Z. Zhang, D.Y. Yeung, J.T. Kwok, E.Y. Chang. Sliced coordinate analysis for effective dimension reduction and nonlinear extensions. Journal of Computational and Graphical Statistics. 17(1): 225-242, Mar 2008.

    2007

  143. S.J. Pan, J.T. Kwok, Q. Yang, J. Pan. Adaptive localization in a dynamic WiFi environment through multi-view learning. Twenty-Second AAAI Conference on Artificial Intelligence (AAAI), pp.1108-1113, Vancouver, British Columbia, Canada. July 2007.

  144. I.W. Tsang, A. Kocsor, J.T. Kwok. Simpler core vector machines with enclosing balls. Twenty-Fourth International Conference on Machine Learning (ICML), pp.911-918, Corvallis, Oregon, USA, June 2007.

  145. K. Zhang, I.W. Tsang, J.T. Kwok. Maximum margin clustering made practical. Twenty-Fourth International Conference on Machine Learning (ICML), pp.1119-1126, Corvallis, Oregon, USA, June 2007.

  146. J.T. Kwok, P.-M. Cheung. Marginalized multi-instance kernels. Twentieth International Joint Conference on Artificial Intelligence (IJCAI), pp.901-906, Hyderabad, India, January 2007.

  147. I.W. Tsang, J.T. Kwok. Ensembles of partially trained SVMs with multiplicative updates. Twentieth International Joint Conference on Artificial Intelligence (IJCAI), pp.1089-1094, Hyderabad, India, January 2007.

  148. Z. Zhang, J.T. Kwok, D.Y. Yeung. Surrogate maximization/minimization algorithms and extensions. Machine Learning, 69(1): 1-33, Oct 2007.

  149. F. Wang, J. Wang, C. Zhang, J.T. Kwok. Face recognition using spectral features. Pattern Recognition, 40(10): 2786-2797, Oct 2007.

  150. J. Park, D. Kang, J. Kim, J.T. Kwok, I.W. Tsang. SVDD-based pattern de-noising. Neural Computation, 19(7): 1919-1938, July 2007.

  151. J.T. Kwok, I.W. Tsang, J.M. Zurada. A class of single-class minimax probability machines for novelty detection. IEEE Transactions on Neural Networks, 18(3): 778-785, May 2007.

    2006

  152. I.W. Tsang, J.T. Kwok. Large-scale sparsified manifold regularization. Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2006. [oral]

  153. K. Zhang, J.T. Kwok. Simplifying mixture models through function approximation. Neural Information Processing Systems (NIPS), Vancouver, Canada, December 2006.

  154. S. Li, C. Liao, J.T. Kwok. Gene feature extraction using T-test statistics and kernel partial least squares. International Conference on Neural Information Processing (ICONIP), pp.11-20, Hong Kong, October 2006.

  155. I.W. Tsang, A. Kocsor, J.T. Kwok. Diversified SVM ensembles for large data sets. European Conference on Machine Learning (ECML), pp.792-800, Berlin, Germany, September 2006.

  156. I.W. Tsang, A. Kocsor, J.T. Kwok. Efficient kernel feature extraction for massive data sets. International Conference on Knowledge Discovery and Data Mining (KDD), pp.724-729, Philadelphia, PA, USA, August 2006.

  157. S. Li, J. Peng, J.T. Kwok, J. Zhang. Multimodal registration using the discrete wavelet frame transform. International Conference on Pattern Recognition (ICPR), pp.877-880, Hong Kong, China, August 2006.

  158. I.W. Tsang, J.T. Kwok. Learning the kernel in Mahalanobis one-class support vector machines. International Joint Conference on Neural Networks (IJCNN), pp.1169-1175, Vancouver, Canada, July 2006.

  159. S. Li, C. Liao, J.T. Kwok. Wavelet-based feature extraction for microarray data classification. International Joint Conference on Neural Networks (IJCNN), pp.5028-5033, Vancouver, Canada, July 2006.

  160. K. Chen, B.-L. Lu, J.T. Kwok. Efficient classification of multi-label and imbalanced data using Min-Max modular classifiers. International Joint Conference on Neural Networks (IJCNN), pp.1770-1775, Vancouver, Canada, July 2006.

  161. P.-M. Cheung, J.T. Kwok. A regularization framework for multiple-instance learning. Twenty-Third International Conference on Machine Learning (ICML), vol 3, pp.193-200, Pittsburgh, PA, USA, June 2006. (abstract) (data)

  162. J. Dai, S. Yan, X. Tang, J.T. Kwok. Locally adaptive classification piloted by uncertainty. Twenty-Third International Conference on Machine Learning (ICML), pp.225-232, Pittsburgh, PA, USA, June 2006. (abstract)

  163. K. Zhang, J.T. Kwok. Block-quantized kernel matrix for fast spectral embedding. Twenty-Third International Conference on Machine Learning (ICML), pp.1097-1104, Pittsburgh, PA, USA, June 2006. (abstract)

  164. K. Zhang, J.T. Kwok. Fitting kernel density estimators using divide-and-conquer. ECCV Workshop on Computation Intensive Methods for Computer Vision, Graz, Austria, May 2006.

  165. K. Zhang, J.T. Kwok, M. Tang. Accelerated convergence using dynamic mean shift. European Conference on Computer Vision (ECCV), pp.257-268, Graz, Austria, May 2006. (abstract)

  166. I.W. Tsang, J.T. Kwok, B. Mak, K. Zhang, J.J. Pan. Fast speaker adaption via maximum penalized likelihood kernel regression. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol 1, pp.997-1000, Toulouse, France, May 2006. (abstract)

  167. J. Park, D. Kang, J.T. Kwok, S.-W. Lee, B.-W. Hwang, S.-W. Lee. Facial image reconstruction by SVDD-based pattern de-noising. International Conference on Biometrics (ICB), pp.129-135, Hong Kong, Jan 2006. (abstract)

  168. I.W. Tsang, J.T. Kwok, J.M. Zurada. Generalized core vector machines. IEEE Transactions on Neural Networks, 17(5):1126-1140, Sept 2006. (abstract)

  169. J.J. Pan, J.T. Kwok, Q. Yang, Y. Chen. Multidimensional vector regression for accurate and low-cost location estimation in pervasive computing. IEEE Transactions on Knowledge and Data Engineering, 18(9): 1181-1193, Sept 2006. (abstract)

  170. H. Zhao, P.C. Yuen, J.T. Kwok. Incremental principal component analysis and its application for face recognition. IEEE Transactions on Systems, Man and Cybernetics (Part B), 36(4):873-886, August 2006. (abstract)

  171. B. Mak, R. Hsiao, S. Ho, J.T. Kwok. Embedded kernel eigenvoice speaker adaptation and its implication to reference speaker weighting. IEEE Transactions on Speech and Audio Processing 14(4):1267-1280, July 2006. (abstract)

  172. Z. Zhang, J.T. Kwok, D.-Y. Yeung. Model-based transductive learning of the kernel matrix. Machine Learning, 63(1):69-101, Apr 2006. (abstract)

  173. I.W. Tsang, J.T. Kwok. Efficient hyperkernel learning using second-order cone programming. IEEE Transactions on Neural Networks, 17(1):48-58, Jan 2006. (abstract)

    2005

  174. K.-F.S. Wong, I.W. Tsang, V. Cheung, S.-H.G. Chan, J.T. Kwok. Position estimation for wireless sensor networks. IEEE Global Telecommunications Conference (GLOBECOM), pp.2772-2776, St. Louis, MO, USA, Nov 2005. (abstract)

  175. I.W. Tsang, J.T. Kwok, K.T. Lai. Core vector regression for very large regression problems. Twenty-Second International Conference on Machine Learning (ICML), pp.913-920, Bonn, Germany, August 2005. (abstract)

  176. J.J. Pan, J.T. Kwok, Q. Yang, Y. Chen. Accurate and low-cost location estmation using kernels. Nineteenth International Joint Conference on Artificial Intelligence (IJCAI), pp.1366-1371, Edinburgh, Scotland, July 2005. (abstract)

  177. I.W. Tsang, P.-M. Cheung, J.T. Kwok. Kernel relevant component analysis for distance metric learning. International Joint Conference on Neural Networks (IJCNN), pp.954-959, Montreal, Canada, July 2005. (abstract)

  178. J. Park, D. Kang, J. Kim, J.T. Kwok, I.W. Tsang. Pattern de-noising based on support vector data description. International Joint Conference on Neural Networks (IJCNN), pp.949-953, Montreal, Canada, July 2005. (abstract)

  179. J. Wang, J.T. Kwok, H.C. Shen, L. Quan. Data-dependent kernels for small-scale, high-dimensional data classification. International Joint Conference on Neural Networks (IJCNN), pp.102-107, Montreal, Canada, July 2005. (abstract)

  180. K. Zhang, M. Tang, J.T. Kwok. Applying neighborhood consistency for fast clustering and kernel density estimation. International Conference on Computer Vision and Pattern Recognition (CVPR), pp.1001-1007, San Diego, CA, USA, June 2005. (abstract)

  181. I.W. Tsang, J.T. Kwok, P.-M. Cheung. Very large SVM training using core vector machines. Tenth International Workshop on Artificial Intelligence and Statistics (AISTATS), Barbados, January 2005. (abstract)

  182. B. Mak, J.T. Kwok, S. Ho. Kernel eigenvoice speaker adaptation. IEEE Transactions on Speech and Audio Processing, 13(5):984-992, Sept 2005. (abstract)

  183. I.W. Tsang, J.T. Kwok, P.-M. Cheung. Core vector machines: Fast SVM training on very large data sets. Journal of Machine Learning Research, 6(Apr):363-392, 2005. (abstract, code)
    • Authors' Reply to the "Comments on the Core Vector Machines: Fast SVM Training on Very Large Data Set"

    2004

  184. H. Zhao, P.C. Yuen, J.T. Kwok, J. Yang. Incremental PCA-based face recognition. International Conference on Control, Automation, Robotics and Vision (ICARCV), Kunming, China, December 2004.

  185. S. Li, J.T. Kwok. Text extraction using edge detection and morphological dilation. International Symposium on Intelligent Multimedia, Video and Speech Processing, Hong Kong, China, October 2004.

  186. B. Mak, S. Ho, J.T. Kwok. Speedup of kernel eigenvoice speaker adaptation by embedded kernel PCA. International Conference on Spoken Language Processing (INTERSPEECH-ICSLP), vol 4, pp.2913-2916, Jeju, Korea, October 2004. (abstract)

  187. I.W. Tsang, J.T. Kwok. Efficient hyperkernel learning using second-order cone programming. European Conference on Machine Learning (ECML), pp.453-464, Pisa, Italy, September 2004. (abstract)

  188. Z. Zhang, K.L. Chan, J.T. Kwok, D.-Y. Yeung. Bayesian inference on principal component analysis using reversible jump Markov chain Monte Carlo. Nineteenth National Conference on Artificial Intelligence (AAAI), pp.372-377, San Jose, California, USA, July 2004. (abstract)

  189. C.S. Chu, I.W. Tsang, J.T. Kwok. Scaling up support vector data description by using core-sets. International Joint Conference on Neural Networks (IJCNN), pp.425-430, Budapest, Hungary, July 2004. (abstract)

  190. Z. Zhang, J.T. Kwok, D.-Y. Yeung. Surrogate maximization/minimization algorithms for AdaBoost and the logistic regression model. Twenty-First International Conference on Machine Learning (ICML), pp.927-934, Banff, Alberta, Canada, July 2004. (abstract)

  191. Z. Zhang, D.-Y. Yeung, J.T. Kwok. Bayesian inference for transductive learning of kernel matrix using the Tanner-Wong data augmentation algorithm. Twenty-First International Conference on Machine Learning (ICML), pp.935-942, Banff, Alberta, Canada, July 2004. (abstract)

  192. B. Mak, J.T. Kwok, S. Ho. Investigation of various composite kernels for kernel eigenvoice speaker adaptation. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), vol 1, pp.325-328, Montreal, Canada, May 2004. (abstract)

  193. J.T. Kwok, B. Mak, S. Ho. Eigenvoice speaker adaptation via composite kernel principal component analysis. Neural Information Processing Systems 16 (NIPS), S. Thrun, L. Saul and B. Schoelkopf, Eds. MIT Press, Cambridge, MA, 2004. (abstract) (slides)

  194. J.T. Kwok, I.W. Tsang. The pre-image problem in kernel methods. IEEE Transactions on Neural Networks, 15(6):1517-1525, Nov 2004. (abstract, code)

  195. S. Li, J.T. Kwok, I.W. Tsang, Y. Wang. Fusing images with different focuses using support vector machines. IEEE Transactions on Neural Networks, 15(6):1555-1561, Nov 2004. (abstract)

  196. V. Cheng, C.-H. Li, J.T. Kwok, C.-K. Li. Dissimilarity learning for nominal data. Pattern Recognition, 37(7):1471-1477, July 2004. (abstract)

    2003

  197. J.T. Kwok, I.W. Tsang. The pre-image problem in kernel methods. Twentieth International Conference on Machine Learning (ICML), pp.408-415, Washington, D.C., USA, August 2003. (abstract) (slides)

  198. J.T. Kwok, I.W. Tsang. Learning with idealized kernels. Twentieth International Conference on Machine Learning (ICML), pp.400-407, Washington, D.C., USA, August 2003. (abstract) (slides)

  199. Z. Zhang, J.T. Kwok, D.Y. Yeung. Parametric distance metric learning with label information. Eighteenth International Joint Conference on Artificial Intelligence (IJCAI), pp.1450-1452, Acapulco, Mexico, August 2003. (abstract)

  200. I.W. Tsang, J.T. Kwok. Distance metric learning with kernels. International Conference on Artificial Neural Networks (ICANN), pp.126-129, Istanbul, Turkey, June 2003. (abstract)

  201. J.T. Kwok, H. Zhao. Incremental eigen decomposition. International Conference on Artificial Neural Networks (ICANN), pp.270-273, Istanbul, Turkey, June 2003. (abstract)

  202. S. Li, J.T. Kwok, H. Zhu, Y. Wang. Texture classification using support vector machines. Pattern Recognition, 36(12):2883-2893, Dec 2003. (abstract)

  203. J.T. Kwok, I.W. Tsang. Linear dependency between epsilon and the input noise in epsilon-support vector regression. IEEE Transactions on Neural Networks, 14(3):544-553, May 2003. (abstract)

  204. K.W. Cheung, J.T. Kwok, M.H. Law, K.C. Tsui. Mining customer product ratings for personalized marketing. Decision Support Systems, 35(2): 231-243, May 2003. (abstract)

    2002

  205. J.T. Kwok, I.W. Tsang. Finding the pre-images in kernel principal component analysis. 6th Annual Workshop On Kernel Machines, NIPS, Whistler, Canada, December 2002. (abstract)

  206. S. Li, J.T. Kwok, Y. Wang. Fusing images with multiple focuses using support vector machines. International Conference on Artificial Neural Networks (ICANN), pp.405-410, Madrid, Spain, August 2002. (abstract)

  207. H. Zhu, J.T. Kwok, L. Qu. Improving de-noising by coefficient de-noising and dyadic wavelet transform. International Conference on Pattern Recognition (ICPR), pp.272-276, Quebec City, Canada, August 2002. (abstract)

  208. S. Li, J.T. Kwok, Y. Wang. Multifocus image fusion using artificial neural networks. Pattern Recognition Letters, 23(8): 985-997, June 2002. (abstract)

  209. S. Li, J.T. Kwok, Y. Wang. Using the discrete wavelet frame transform to merge Landsat TM and SPOT panchromatic images. Information Fusion, 3(1):17-23, March, 2002. (abstract)

    2001

  210. M.H. Law, J.T. Kwok. Applying the Bayesian evidence framework to nu-support vector regression. Twelfth European Conference on Machine Learning (ECML), pp.312-323, Freiburg, Germany, September 2001. (abstract)

  211. J.T. Kwok. Linear dependency between epsilon and the input noise in epsilon-support vector regression. International Conference on Artificial Neural Networks (ICANN), pp.405-410, Vienna, Austria, August 2001. (abstract) (@ Springer-Verlag)

  212. M.H. Law, J.T. Kwok. Bayesian support vector regression. Eighth International Workshop on Artificial Intelligence and Statistics (AISTATS), pp.239-244, Key West, Florida, USA, January 2001. (abstract)

  213. S. Li, J.T. Kwok, Y. Wang. Combination of images with diverse focuses using the spatial frequency. Information Fusion, 2(3):169-176, September 2001. (abstract)

    2000

  214. M.H. Law, J.T. Kwok. Rival penalized competitive learning for model-based sequence clustering. International Conference on Pattern Recognition (ICPR), vol 2, pp.195-198, Barcelona, Spain, September 2000. (abstract)

  215. J.J. Liu, J.T. Kwok. An extended genetic rule induction algorithm. Congress on Evolutionary Computation (CEC), pp.458-463, La Jolla, California, USA, July 2000. (abstract)

  216. W.K. Cheung, J.T. Kwok, M.H. Law, K.C. Tsui. Mining customer preference ratings for product recommendation using the support vector machine and the latent class model. Second International Conference on Data Mining Methods and Databases for Engineering, Finance and Other Fields, pp.601-610, Cambridge, UK, July 2000. (abstract)

  217. J.T. Kwok. The evidence framework applied to support vector machines. IEEE Transactions on Neural Networks, 11(5):1162-1173, September 2000. (abstract)

    1999

  218. J.T. Kwok. Moderating the outputs of support vector machine classifiers. International Joint Conference on Neural Networks (IJCNN), pp.943-948, Washington, DC, USA, July 1999. (abstract)

  219. J.T. Kwok. Integrating the evidence framework and the support vector machine. European Symposium on Artificial Neural Networks (ESANN), pp.177-182, Bruges, Belgium, April 1999. (abstract)

  220. J.T. Kwok. Moderating the outputs of support vector machine classifiers. IEEE Transactions on Neural Networks, 10(5):1018-1031, September 1999. (abstract)

    1998

  221. J.T. Kwok. Automated text categorization using support vector machine. International Conference on Neural Information Processing (ICONIP), pp.347-351, Kitakyushu, Japan, October 1998. (abstract)

  222. J.T. Kwok. Support vector mixture for classification and regression problems. International Conference on Pattern Recognition (ICPR), pp.255-258, Brisbane, Australia, August 1998. (abstract)

    1997

  223. T.Y. Kwok, D.Y. Yeung. Objective functions for training new hidden units in constructive neural networks. IEEE Transactions on Neural Networks, 8(5):1131-1148, September 1997.

  224. T.Y. Kwok, D.Y. Yeung. Constructive algorithms for structure learning in feedforward neural networks for regression problems. IEEE Transactions on Neural Networks, 8(3):630-645, May 1997.

    1996

  225. T.Y. Kwok, D.Y. Yeung. Reference priors for neural networks: Laplace versus Gaussian. International Conference on Neural Information Processing (ICONIP), pp.109-114, Hong Kong, September 1996.

  226. T.Y. Kwok, D.Y. Yeung. Bayesian regularization in constructive neural networks. International Conference on Artificial Neural Networks (ICANN), pp.557-562, Bochum, Germany, July 1996.

  227. T.Y. Kwok, D.Y. Yeung. Use of bias term in projection pursuit learning improves approximation and convergence properties. IEEE Transactions on Neural Networks, 7(5):1168-1183, September 1996.

    1995

  228. T.Y. Kwok, D.Y. Yeung. Efficient cross-validation for feedforward neural networks. IEEE International Conference on Neural Networks (ICNN), Perth, Western Australia, November 1995.

  229. T.Y. Kwok, D.Y. Yeung. Improving the approximation and convergence capabilities of projection pursuit learning. International Conference on Artificial Neural Networks (ICANN), pp.197-202, Paris, France, October 1995.

  230. T.Y. Kwok, D.Y. Yeung. Improving the approximation and convergence capabilities of projection pursuit learning. Neural Processing Letters, 2(3):20-25, May 1995.

    1994

  231. T.Y. Kwok, D.Y. Yeung. Constructive neural networks: Some practical considerations. IEEE International Conference on Neural Networks (ICNN), pp.198-203, Orlando, Florida, USA, June 1994.

  232. T.Y. Kwok, D.Y. Yeung. A theoretically sound learning algorithm for constructive neural networks. IEEE International Symposium on Speech, Image Processing and Neural Networks, pp.389-392, Hong Kong, April 1994.

    1993

  233. T.Y. Kwok, D.Y. Yeung. Experimental analysis of input weight freezing in constructive neural networks. IEEE International Conference on Neural Networks (ICNN), pp.511-516, San Francisco, California, USA, March 1993.

Edited Books

  1. J. Kim, J. Kwok, K. Sumiya, B.-T. Zhang (Eds.): Special issue on the First International Conference on Big Data and Smart Computing 2014. Data Knowledge Engineering, 104: 15-16 2016.

  2. J.T. Kwok, Z.H. Zhou (Eds.): Special Issue on Sino-foreign-interchange Conference on Intelligence Science and Intelligent Data 2013. Neurocomputing, 2015.

  3. J.T. Kwok, L. Zhang, H. Lu (Eds.): Selected papers from the 2011 International Conference on Neural Information Processing (ICONIP 2011). Neurocomputing 129, 2014.

  4. L. Zhang, J.T. Kwok, C. Zhang (Eds.): Special Issue for ISNN 2010. Neurocomputing 76, 2012.

  5. B.-L. Lu, L. Zhang, J.T. Kwok (Eds.): Neural Information Processing - 18th International Conference, Proceedings, Springer June 2011.

  6. L. Zhang, B.-L. Lu, J.T. Kwok (Eds.): Advances in Neural Networks: 7th International Symposium on Neural Networks, ISNN 2010. Springer June 2010.

  7. N. da Vitoria Lobo, T. Kasparis, F. Roli, J.T. Kwok, M. Georgiopoulos, G.C. Anagnostopoulos, M. Loog (Eds.): Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshops, SSPR & SPR 2008. Springer, December 2008.

  8. D.Y. Yeung, J.T. Kwok, A. Fred, F. Roli, D. de Ridder (eds.). Structural, Syntactic and Statistical Pattern Recognition: Joint IAPR International Workshops, SSPR 2006 and SPR 2006. Springer, August 2006.

Book Chapters

  1. J.T. Kwok, Z.-H. Zhou, L. Xu. Machine Learning. Handbook of Computational Intelligence: pp.495-522, Springer 2015.