Yu-Yang Qian, Yi-Han Wang, Zhen-Yu Zhang, Yuan Jiang, and Zhi-Hua Zhou. Adapting to Generalized Online Label Shift by Invariant Representation Learning. In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25), Toronto, Canada, 2025, to appear.
Lanjihong Ma, Yao-Xiang Ding, Zhen-Yu Zhang, and Zhi-Hua Zhou. Achieving Nearly-Optimal Regret and Sample Complexity in Dueling Bandits. In: Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'25), Toronto, Canada, 2025, to appear.
Yan-Feng Xie, Peng Zhao, and Zhi-Hua Zhou. Gradient-Variation Online Learning under Generalized Smoothness. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, to appear.
Yu-Hu Yan, Peng Zhao, and Zhi-Hua Zhou. A Simple and Optimal Approach for Universal Online Learning with Gradient Variations. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, to appear.
Long-Fei Li, Peng Zhao, and Zhi-Hua Zhou. Near-Optimal Dynamic Regret for Adversarial Linear Mixture MDPs. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, to appear.
Long-Fei Li, Yu-Jie Zhang, Peng Zhao, and Zhi-Hua Zhou. Provably Efficient Reinforcement Learning with Multinomial Logit Function Approximation. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, to appear.
Peng Tan, Hai-Tian Liu, Zhi-Hao Tan, and Zhi-Hua Zhou. Handling Learnwares from Heterogeneous Feature Spaces with Explicit Label Exploitation. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, to appear.
Hao-Yi Lei, Zhi-Hao Tan, and Zhi-Hua Zhou. On the Ability of Developers' Training Data Preservation of Learnware. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, to appear.
Wen-Bo Du, Tian Qin, Tian-Zuo Wang, and Zhi-Hua Zhou. Avoiding Undesired Future with Minimal Cost in Non-Stationary Environments. In: Advances in Neural Information Processing Systems 37 (NeurIPS'24), Vancouver, Canada, 2024, to appear.
Zhi-Hao Tan, Jian-Dong Liu, Xiao-Dong Bi, Peng Tan, Qin-Cheng Zheng, Hai-Tian Liu, Yi Xie, Xiao-Chuan Zou, Yang Yu, and Zhi-Hua Zhou. Beimingwu: A Learnware Dock System. In: Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'24), Barcelona, Spain, 2024, pp.5773-5782. Jing Wang, Miao Yu, Peng Zhao, and Zhi-Hua Zhou. Learning with Adaptive Resource Allocation. In: Proceedings of the 41st International Conference on Machine Learning (ICML'24), Vienna, Austria, 2024, pp.52099-52116. Yu-Xuan Huang, Zequn Sun, Guangyao Li, Xiaobin Tian, Wang-Zhou Dai, Wei Hu, Yuan Jiang, and Zhi-Hua Zhou. Enabling Abductive Learning to Exploit Knowledge Graph. In: Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI'23), Macao, China, 2023, pp.3839-3847. Zhen-Yu Zhang, Yu-Yang Qian, Yu-Jie Zhang, Yuan Jiang, and Zhi-Hua Zhou. Adaptive Learning for Weakly Labeled Streams. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), Washington, DC, 2022, pp.2556-2564. [code] Jiachen Wang, Dazhen Deng, Xiao Xie, Xinhuan Shu, Yu-Xuan Huang, Le-Wen Cai, Hui Zhang, Min-Ling Zhang, Zhi-Hua Zhou, and Yingcai Wu. Tac-Valuer: Knowledge-based Stroke Evaluation in Table Tennis. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'21), Online, 2021, pp.3688-3696. Yu-Xuan Huang, Wang-Zhou Dai, Jian Yang, Le-Wen Cai, Shaofeng Cheng, Ruizhang Huang, Yu-Feng Li, and Zhi-Hua Zhou. Semi-supervised abductive learning and its application to theft judicial sentencing. In: Proceedings of the 20th IEEE International Conference on Data Mining (ICDM'20), Online, 2020, pp.1070-1075. Liang Yang, Xi-Zhu Wu, Yuan Jiang, and Zhi-Hua Zhou. Multi-label deep forest. In: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI'20), Santiago de Compostela, Spain, 2020, pp.1634-1641. [code] Peng Zhao, Lijun Zhang, Yuan Jiang, and Zhi-Hua Zhou. A simple approach for non-stationary linear bandits. In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS'20), Online [Palermo, Italy], 2020, pp.746-755. Wen-Ji Zhou, Yang Yu, Yingfeng Chen, Kai Guan, Tangjie Lv, Changjie Fan, and Zhi-Hua Zhou. Reinforcement learning experience reuse with policy residual representation. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, 2019, pp.4447-4453. Kai Ming Ting, Yue Zhu, and Zhi-Hua Zhou. Isolation kernel and its effect to SVM. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18), London, UK, 2018, pp.2329-2337. Dong-Dong Chen, Wei Wang, Wei Gao, and Zhi-Hua Zhou. Tri-net for semi-supervised deep learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.2014-2020. [code] Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, and Zhi-Hua Zhou. Dual set multi-label learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, pp.3635-3642. [code] Chao Qian, Jing-Cheng. Shi, Yang Yu, Ke Tang, and Zhi-Hua Zhou. Subset selection under noise. In: Advances in Neural Information Processing Systems 30 (NIPS'17), (Long Beach, CA), 2017, pp.3563-3573. [code] Ya-Lin Zhang, Longfei Li, Jun Zhou, Xiaolong Li, Yujiang Liu, Yuanchao Zhang, and Zhi-Hua Zhou. POSTER: A PU Learning based System for Potential Malicious URL Detection. In: Proceedings of the 24th ACM SIGSAC Conference on Computer and Communications Security (CCS'17), Dallas, TX, 2017, pp.2599-2601. Miao Xu and Zhi-Hua Zhou. Incomplete label distribution learning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3175-3181. [code] Bo-Jian Hou, Lijun Zhang, and Zhi-Hua Zhou. Storage fit learning with unlabeled data. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.1844-1850. [code] Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, and Zhi-Hua Zhou. Optimizing ratio of monotone set functions. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.2606-2612. [code] Xiu-Shen Wei, Chen-Lin Zhang, Y. Li, Chen-Wei Xie, Jianxin Wu, Chunhua Shen, and Zhi-Hua Zhou. Deep descriptor transforming for image co-localization. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3045-3054. [code] Ji Feng and Zhi-Hua Zhou. DeepMIML network. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, pp.1884-1890. Yang Yang, De-Chuan Zhan, Ying Fan, Yuan Jiang, and Zhi-Hua Zhou. Deep learning for fixed model reuse. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017, pp.2831-2837. Han-Jia Ye, De-Chuan Zhan, Xue-Min Si, Yuan Jiang, and Zhi-Hua Zhou. What makes objects similar: A unified multi-metric learning approach. In: Advances in Neural Information Processing Systems 29 (NIPS'16), (Barcelona, Spain), D. D. Lee, M. Sugiyama, U. V. Luxburg, I. Guyon, R. Garnett, eds. Cambridge, MA: MIT Press, 2016, pp.1235-1243. Wei Gao, Xin-Yi Niu, and Zhi-Hua Zhou. Learnability of non-i.i.d. In: Proceedings of the 8th Asian Conference on Machine Learning (ACML'16), Hamilton, New Zealand, JMLR: WCP 63, 2016, pp.158-173. De-Chuan Zhan, Peng Hu, Zui Chu, and Zhi-Hua Zhou. Learning expected hitting time distance. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016, pp.2309-2314. Lijun Zhang, Tianbao Yang, Rong Jin, and Zhi-Hua Zhou. Stochastic optimization for kernel PCA. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016, pp.2315-2322. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Subset selection by pareto optimization. In: Advances in Neural Information Processing Systems 28 (NIPS'15), (Montreal, Canada), C. Cortes, N. D. Lawrence, D. D. Lee, M. Sugiyama, R. Garnett, eds. Cambridge, MA: MIT Press, 2015, pp.1765-1773. Wang-Zhou Dai and Zhi-Hua Zhou. Statistical unfolded logic learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML'15), Hong Kong, JMLR: WCP 45, 2015, pp. 349-361. Yue Zhu, Wei Gao, and Zhi-Hua Zhou. One-pass multi-view learning. In: Proceedings of the 7th Asian Conference on Machine Learning (ACML'15), Hong Kong, JMLR: WCP 45, 2015, pp.407-422. Chao Qian, Yang Yu, and Zhi-Hua Zhou. On constrained boolean pareto optimization. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.389-395. Jinhong Zhong, Ke Tang, and Zhi-Hua Zhou. Active learning from crowds with unsure option. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.1061-1067. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp. 2935-2941. Xiu-Shen Wei, Jianxin Wu, and Zhi-Hua Zhou. Scalable multi-instance learning. In: Proceedings of the 14th IEEE International Conference on Data Mining (ICDM'14), Shenzhen, China, 2014, pp.1037-1042. [code]
Nan Li, Rong Jin, and Zhi-Hua Zhou. Top rank optimization in linear time. In: Advances in Neural Information Processing Systems 27 (NIPS'14), (Montreal, Canada), Z. GhahramaM. Welling, C. Cortes, N. D. Lawrence, K. Q. Weinberger, eds. Cambridge, MA: MIT Press, 2014, pp. 1502-1510. Yue Zhu, Jianxin Wu, Yuan Jiang, and Zhi-Hua Zhou. Learning with augmented multi-instance view. In: Proceedings of the 6th Asian Conference on Machine Learning (ACML'14), Nha Trang, Vietnam, JMLR: WCP 39, 2014, pp.234-249. Zhi-Hua Zhou. Large margin distribution learning. In: Proceedings of the 6th IAPR International Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR'14), Montreal, Canada, LNAI 8774, 2014, pp.1-11. (keynote article) [code][slides] Shao-Yuan Li, Yuan Jiang, and Zhi-Hua Zhou. Partial multi-view clustering. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), Quebec City, Canada, 2014, pp.1968-1974. [code]
Tianshi Chen, Qi Guo, Ke Tang, Olivier Temam, Zhiwei Xu, Zhi-Hua Zhou, and Yunji Chen. ArchRanker: A ranking approach to design space exploration. In: Proceedings of the 41st International Symposium on Computer Architecture (ISCA'14), Minneapolis, MN, 2014, pp.85-96. Miao Xu, Rong Jin, and Zhi-Hua Zhou. Speedup matrix completion with side information: Application to multi-label learning. In: Advances in Neural Information Processing Systems 26 (NIPS'13), (Lake Tahoe, NV), C. J. C. Burges, L. Bottou, M. Welling, Z. Ghahramani, K. Q. Weinberger, eds. Cambridge, MA: MIT Press, 2013, pp.2301-2309. Wei Wang and Zhi-Hua Zhou. Co-training with insufficient views. In: Proceedings of the 5th Asian Conference on Machine Learning (ACML'13), Canberra, Australia, JMLR: WCP 29, 2013, pp.467-482. Miao Xu, Yu-Feng Li, and Zhi-Hua Zhou. Multi-label learning with PRO loss. In: Proceedings of the 27th AAAI Conference on Artificial Intelligence (AAAI'13), Bellevue, WA, 2013, pp.998-1004. [code] Tianbao Yang, Yu-Feng Li, M. Mahdavi, Rong Jin, and Zhi-Hua Zhou. Nystrom method vs random Fourier features: A theoretical and empirical comparison. In: Advances in Neural Information Processing Systems 25 (NIPS'12), (Lake Tahoe, NV), P. Bartlett, F. C. N. Pereira, C. J. C. Burges, L. Bottou, K. Q. Weinberger, eds. Cambridge, MA: MIT Press, 2012, pp.485-493. Nan Li, Yang Yu, and Zhi-Hua Zhou. Diversity regularized ensemble pruning. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'12), Bristol, UK, LNCS 7523, 2012, pp330-345. [code] Sheng-Jun Huang, Yang Yu, and Zhi-Hua Zhou. Multi-label hypothesis reuse. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'12), Beijing, China, 2012, pp.525-533. [code] Bo Wang, Jiayan Jiang, Wei Wang, Zhi-Hua Zhou, and Zhuowen Tu. Unsupervised metric fusion by cross diffusion. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'12), Providence, RI, 2012, pp.2997-3004. Yong Ge, Hui Xiong, Chuanren Liu, and Zhi-Hua Zhou. A taxi driving fraud detection system. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM'11), Vancouver, Canada, 2011, pp.181-190. Yong Wang, Yuan Jiang, Yi Wu, and Zhi-Hua Zhou. Localized K-flats. In: Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI'11), San Francisco, CA, 2011, pp.523-530. Yang Yu, Yu-Feng Li, and Zhi-Hua Zhou. Diversity regularized machine. In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp.1603-1608. [code]
Wei Wang and Zhi-Hua Zhou. Multi-view active learning in the non-realizable case. In: Advances in Neural Information Processing Systems 23 (NIPS'10), (Vancouver, Canada), J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta, eds. Cambridge, MA: MIT Press, 2010, pp.2388-2396. Sheng-Jun Huang, Rong Jin, and Zhi-Hua Zhou. Active learning by querying informative and representative examples. In: Advances in Neural Information Processing Systems 23 (NIPS'10), (Vancouver, Canada), J. Lafferty, C. K. I. Williams, J. Shawe-Taylor, R. S. Zemel, A. Culotta,
eds. Cambridge, A. Culotta, eds. CambridgeMA: MIT Press, 2010, 892-900. Y. Ge, H. Xiong, Zhi-Hua Zhou, H. Ozdemir, J. Yu, and K. C. Lee. TOP-EYE: Top-k evolving trajectory outlier detection. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management (CIKM'10), Toronto, Canada, 2010, pp.1733-1736. (short paper) Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. On detecting clustered anomalies using SCiForest. In: Proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML PKDD'10), Barcelona, Spain, LNAI 6322, 2010, pp.274-290. Wei Gao and Zhi-Hua Zhou. Approximation stability and boosting. In: Proceedings of the 21st International Conference on Algorithmic Learning Theory (ALT'10), LNCS 6331, Canberra, Australia, 2010, pp.59-73. Y. Wang, Yuan Jiang, Y. Wu, and Zhi-Hua Zhou. Multi-manifold clustering. In: Proceedings of the 11th Pacific Rim International Conference on Artificial Intelligence (PRICAI'10), LNAI 6230, Daegu, Korea, 2010, pp.280-291. [slides]
This paper won the Best Paper Award at
PRICAI'10 Xu-Ying Liu and Zhi-Hua Zhou. Learning with cost intervals. In: Proceedings of the 16th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'10), Washington, DC, 2010, pp.403-412. [code] Wei Wang and Zhi-Hua Zhou. A new analysis of co-training. In: Proceedings of the 27th International Conference on Machine Learning (ICML'10), Haifa, Israel, 2010, pp.1135-1142. Zhi-Hua Zhou and Nan Li. Multi-information ensemble diversity. In: Proceedings of the 9th International Workshop on Multiple Classifier Systems (MCS'10), LNCS 5997, Cairo, Egypt, 2010, pp.134-144. [code] Yin Zhang and Zhi-Hua Zhou. Non-metric label propagation. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI'09), Pasadena, CA, 2009, pp.1357-1362. [code] Yu-Feng Li, Ivor W. Tsang, James T. Kwok, and Zhi-Hua Zhou. Tighter and convex maximum margin clustering. In: Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS'09), Clearwater Beach, FL, 2009, pp.328-335. [code] Fei Tony Liu, Kai Ming Ting, and Zhi-Hua Zhou. Isolation forest. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.413-422. [code]
This paper won the Theoretical/Algorithms Runner-Up
Best Paper Award at IEEE ICDM'08 Yin Zhang and Zhi-Hua Zhou. Cost-sensitive face recognition. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'08), Anchorage, AK, 2008. [code] Zhi-Hua Zhou and Hong-Bin Dai. Exploiting image contents in web search. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI'07), Hyderabad, India, 2007, pp.2928-2933. Wei Wang and Zhi-Hua Zhou. Analyzing co-training style algorithms. In: Proceedings of the 18th European Conference on Machine Learning (ECML'07), Warsaw, Poland, LNAI 4701, 2007, pp.454-465. Yang Yu, Zhi-Hua Zhou, and Kai Ming Ting. Cocktail ensemble for regression. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM'07), Omeha, NE, 2007, pp.721-726. Daoqiang Zhang, Zhi-Hua Zhou, and Shifu Chen. Non-negative matrix factorization on kernels. In: Proceedings of the 9th Pacific Rim International Conference on Artificial Intelligence (PRICAI'06), Guilin, China, LNAI 4099, 2006, pp.404-412. [data]
This paper won the Best Paper Award at
PRICAI'06 Zhi-Hua Zhou and Min-Ling Zhang. Ensembles of multi-instance learners. In: Proceedings of the 14th European Conference on Machine Learning (ECML'03), Cavtat-Dubrovnik, Croatia, LNAI 2837, 2003, pp.492-502. [code] Zhi-Hua Zhou, Jianxin Wu, Yuan Jiang, and Shi-Fu Chen. Genetic algorithm based selective neural network ensemble. In: Proceedings of the 17th International Joint Conference on Artificial Intelligence (IJCAI'01), Seattle, WA, vol.2, 2001, pp.797-802. This paper was nominated along with other four
papers for the Distinguished Paper Award at IJCAI'01 Fu Jie Huang , Zhi-Hua Zhou, Hong-Jiang Zhang, and Tsuhan Chen. Pose invariant face recognition. In: Proceedings of the 4th IEEE International Conference on Automatic Face and Gesture Recognition (FG'00), Grenoble, France, 2000, pp.245-250. Lanjihong Ma, Yao-Xiang Ding, Peng Zhao, and Zhi-Hua Zhou. Learning Objective Adaptation by Correlation-based Model Reuse. IEEE Transactions on Neural Networks and Learning Systems, in press.
Han Hu, Chao Qian, Ke Xue, Rainer Georg Jörgensen, Marco Keiluweit, Chao Liang, Xuefeng Zhu, Ji Chen, Yishen Sun, Haowei Ni, Jixian Ding, Weigen Huang, Jingdong Mao, Rong-Xi Tan, Jizhong Zhou, Thomas W Crowther, Zhi-Hua Zhou, Jiabao Zhang, and Yuting Liang. Reducing the uncertainty in estimating soil microbial-derived carbon storage. Proceedings of the National Academy of Sciences, 2024, 121(35): e2401916121. Jin-Hui Wu, Shao-Qun Zhang, Yuan Jiang, and Zhi-Hua Zhou. Theoretical Exploration of Flexible Transmitter Model. IEEE Transactions on Neural Networks and Learning Systems, 2024, 35(3): 3674-3688.
Kai Ming Ting, Bi-Cun Xu, Takashi Washio, and Zhi-Hua Zhou. Isolation Distributional Kernel: A New Tool for Point and Group Anomaly Detections. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(3): 2697-2710.
Guangda Huzhang, Zhen-Jia Pang, Yongqing Gao, Yawen Liu, Weijie Shen, Wen-Ji Zhou, Qianying Lin, Qing Da, AnXiang Zeng, Han Yu, Yang Yu, and Zhi-Hua Zhou. AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(2): 1214-1226.
Shao-Yuan, Yuan Jiang, Nitesh V. Chawla, and Zhi-Hua Zhou. Multi-label learning from crowds. IEEE Transactions on Knowledge and Data Engineering, 2019, 31(7): 1369-1382. Ya-Lin Zhang, Jun Zhou, Wenhao Zheng, Ji Feng, Longfei Li, Ziqi Liu, Ming Li, Zhiqiang Zhang, Chaochao Chen, Xiaolong Li, Yuan (Alan) Qi, Zhi-Hua Zhou. Distributed deep forest and its application to automatic detection of cash-out fraud. ACM Transactions on Intelligent Systems and Technology, 2019, 10(5): Article 55. (CORR abs/1805.04234) Wei Gao, Lu Wang, Rong Jin, S.-H. Zhu, and Zhi-Hua Zhou. One-pass AUC optimization. Artificial Intelligence, 2016, 236: 1-29. Guo-Bing Zhou, Jianxin Wu, Chen-Lin Zhang, and Zhi-Hua Zhou. Minimal gated unit for recurrent neural networks. International Journal of Automation and Computing, 2016, 13(3): 226-234. This article was awarded as the IJAC 2016
Most Cited Paper in 2018 Qi Guo, Tianshi Chen, Zhi-Hua Zhou, Olivier Temam, Ling Li, Depei Qian, and Yunji Chen. Robust design space modeling. ACM Transactions on Design Automation of Electronic Systems, 2015, 20(2): Article 18. Chenfeng He, Ying-Xin Li, Guangxin Zhang, Zuguang Gu, Rong Yang, Jie Li, Zhi John Lu, Zhi-Hua Zhou, Chenyu Zhang, Jin Wang. MiRmat: Mature microRNA sequence prediction. PLOS One, 2012, 7(12): e51673. Yanwei Fu, Yanwen Guo, Yanshu Zhu, Feng Liu, Chuanming Song and Zhi-Hua Zhou. Multi-view video summarization. IEEE Transactions on Multimedia, 2010, 12(7): 717-729. [demo] Xindong Wu, Vipin Kumar, J. Ross Quinlan, Joydeep Ghosh, Qiang Yang, Hiroshi Motoda, Geoffrey J. McLachlan, Angus Ng, Bing Liu, Philip S. Yu, Zhi-Hua Zhou, Michael Steinbach, David J. Hand, and Dan Steinberg. Top 10 algorithms in data mining. Knowledge and Information Systems, 2008, 14(1): 1-37. This
is a summarization article of the ICDM'06 Panel on
"Top 10 Algorithms in Data
Mining" Zhi-Hua Zhou and Yu-Xuan Huang. Abductive learning. In P. Hitzler and M. K. Sarker eds., Neuro-Symbolic Artificial Intelligence: The State of the Art, IOP Press, Amsterdam, 2022, p.353-379 F. Bonchi, J. Domingo-Ferrer, R. Baeza-Yates, Zhi-Hua Zhou, and Xintao Wu, eds. Proceedings of the 16th IEEE International Conference on Data Mining (ICDM'15), IEEE Computer Society Press, 2016. ISBN: 978-1-5090-5472-5
C. Domeniconi, F. Gullo, F. Bonchi, J. Domingo-Ferrer, R. Baeza-Yates, Zhi-Hua Zhou, and Xintao Wu, eds. Proceedings of the 16th International Conference on Data Mining Workshops (ICDMW), IEEE Computer Society Press, 2016. ISBN: 978-1-5090-5472-5
C. Aggarwal, Zhi-Hua Zhou, A. Tuzhilin, H. Xiong, and Xintao Wu, eds. Proceedings of the 15th IEEE International Conference on Data Mining (ICDM'15), IEEE Computer Society Press, 2015. ISBN: 978-1-4673-9504-5
P. Cui, J. Dy, C. Aggarwal, Zhi-Hua Zhou, A. Tuzhilin, H. Xiong, and Xintao Wu, eds. Proceedings of the 15th IEEE International Conference on Data Mining Workshops (ICDMW'15), IEEE Computer Society Press, 2015. ISBN: 978-1-4673-8492-6
Zhi-Hua Zhou, Wei Wang, R. Kumar, H. Toivonen, J. Pei, J. Z. Huang, and Xintao Wu, eds. Proceedings of the 14th IEEE International Conference on Data Mining Workshops (ICDMW'14), IEEE Computer Society Press, 2014. ISBN: 978-1-4799-4275-6
N. Chawla, N. Japkowicz, and Zhi-Hua Zhou, eds. Working Notes of the Workshop on Data Mining When Classes are Imbalanced and Errors Have Costs (ICEC'09), in conjunction with PAKDD'09, Bangkok, Thailand, 2009.
C. Soares, Y. Peng, J. Meng, Takashi Washio, and Zhi-Hua Zhou, eds. Applications of Data Mining in E-Business and Finance - Revised Selected Papers of PAKDD'07 Workshop on Data Mining for Business, Amsterdam, The Netherlands: IOS Press, 2008. ISBN: 978-1-58603-890-8 Takashi Washio, Zhi-Hua Zhou, J. Z. Huang, X. Hu, J. Li, C. Xie, J. He, D. Zou, K.-C. Li, M. M. Freire, eds. Emerging Technologies in Knowledge Discovery and Data Mining (Lecture Notes in Artificial Intelligence 4819) - Revised Selected Papers of PAKDD'07 International Workshops, Berlin: Springer, 2007. ISBN: 978-3-540-77016-9 R. J. Durrant, K.-E. Kim, G. Holmes, S. Marsland, Masashi Sugiyama, and Zhi-Hua Zhou. Foreword: Special issue for the journal track of the 8th Asian Conference on Machine Learning. Machine Learning, 2017, 106(5): 623-625. this article was the editorial to the special issue Journal track of ACML 2016 edited by R. J. Durrant, K.-E. Kim, G. Holmes, S. Marsland, M. Sugiyama, and Z.-H. Zhou G. Holmes, T.-Y. Liu, Hang Li, I. King, Masashi Sugiyama, and Zhi-Hua Zhou. Introduction: Special issue of selected papers from ACML 2015. Machine Learning, 2017, 106(4): 459-461. this article was the editorial to the special issue Selected papers from ACML 2015 edited by G. Holmes, T.-Y. Liu, H. Li, I. King, M. Sugiyama, and Z.-H. Zhou Jianxin Wu, X. Bai, M. Loog, F. Roli, and Zhi-Hua Zhou. Editorial of the special issue on multi-instance learning in pattern recognition and vision. Pattern Recognition, 2017, 71: 444-445. this article was the editorial to the special issue Multi-instance learning in pattern recognition and vision edited by J. Wu, X. Bai, M. Loog, F. Roli, and Z.-H. Zhou Hang Li, D. Phung, T. Cao, T. B. Ho, and Zhi-Hua Zhou. Introduction: Special issue of selected papers from ACML 2014. Machine Learning, 2016, 103(2): 137-139. this article was the editorial to the special issue Selected papers from ACML 2014 edited by H. Li, D. Phung, T. Cao, T. B. Ho, and Z.-H. Zhou Zhi-Hua Zhou, W. S. Lee, S. C. H. Hoi, W. Buntine, and Hiroshi Motoda. Introduction: Special issue of selected papers of ACML 2012. Machine Learning, 2013, 92(2-3): 221-223. this article was the editorial to the special issue Selected papers of ACML 2012 edited by Z.-H. Zhou, W. S. Lee, S. C. H. Hoi, W. Buntine, and H. Motoda J. Cheng, J. Wang, Shuiwang Jiang, Zhi-Hua Zhou, E. Hancock. Special edition on semi-supervised learning for visual content analysis and understanding. Pattern Recognition, 2011, 44(10-11): 2242-2243. this article was the editorial to the special issue Semi-Supervised Learning for Visual Content Analysis and Understanding edited by J. Cheng, J. Wang, S. Jiang, Z.-H. Zhou, and E. Hancock Qiang Yang, Zhi-Hua Zhou, W. Mao, W. Li, and N. Nan Li. Social learning. IEEE Intelligent Systems, 2010, 25(4): 9-11. this article was the editorial to the special issue Social Learning edited by Q. Yang, Z.-H. Zhou, W. Mao, W. Li, and N. N. Li T. B. Ho, Zhi-Hua Zhou, and Hiroshi Motoda. Editorial. Intelligent Data Analysis, 2010, 14(4): 437-438. this article was the editorial to the special section Selected Papers from PRICAI 2008 edited by T. B. Ho, Z.-H. Zhou, and H. Motoda T. B. Ho, Zhi-Hua Zhou, and Hiroshi Motoda. Preface. International Journal of Software and Informatics, 2009, 3(1): 1-2. this article was the editorial to the special section Selected Papers from PRICAI 2008 edited by T. B. Ho, Z.-H. Zhou, and H. Motoda Zhi-Hua Zhou and Min-Ling Zhang. Multi-label learning. In: C. Sammut, G. I. Webb, eds. Encyclopedia of Machine Learning and Data Mining, Berlin: Springer, 2017, 875-881. J. T. Kwok, Zhi-Hua Zhou, and L. Xu. Machine learning. In: J. Kacprzyk, W. Pedrycz, eds. Springer Handbook of Computational Intelligence, Berlin: Springer, 2015, 495-522. K. Zhang, B. Schölkopf, K. Muandet, Z. Wang, Zhi-Hua Zhou, and C. Persello. Single-source domain adaptation with target and conditional shift. In: J. A. K. Suykens, M. Signoretto, A. Argyriou, eds. Regularization, Optimization, Kernels, and Support Vector Machines, Boca Raton, FL: CRC Press, 2014, 428-456. Xu-Ying Liu and Zhi-Hua Zhou. Imbalanced learning. In: H. He, Y. Ma, eds. Imbalanced Learning: Foundations, Algorithms, and Applications, Hoboken, NJ: Wiley-IEEE, 2013, 61-82. Zhi-Hua Zhou. Ensemble learning. In: S. Z. Li ed. Encyclopedia of Biometrics, Berlin: Springer, 2009, 270-273. Zhi-Hua Zhou. Ensemble. In: L. Liu and T. Özsu eds. Encyclopedia of Database Systems, Berlin: Springer, 2009, 988-991. Zhi-Hua Zhou. Boosting. In: L. Liu and T. Özsu eds. Encyclopedia of Database Systems, Berlin: Springer, 2009, 260-263. Zhi-Hua Zhou and Yang Yu. AdaBoost. In: X. Wu and V. Kumar eds. The Top Ten Algorithms in Data Mining, Boca Raton, FL: Chapman & Hall, 2009, 127-149. C. Soares, Y. Peng, J. Meng, Takashi Washio, and Zhi-Hua Zhou. Applications of data mining in e-business and finance: Introduction. In: C. Soares, Y. Peng, J. Meng, T. Washio, and Z.-H. Zhou, eds. Applications of Data Mining in E-Business and Finance, Amsterdam, The Netherlands: IOS Press, 2008, 1-9. Zhi-Hua Zhou. Multi-instance learning: A survey. Technical Report, AI Lab, Department of Computer Science & Technology, Nanjing University, Nanjing, China, Mar. 2004. 周志华, 王魏, 高尉, 张利军 著. 机器学习理论导引, 北京: 机械工业出版社, 2020. (ISBN 978-7-111-65424-7) 周志华 著, 李楠 译. 集成学习: 基础与算法, 北京: 电子工业出版社, 2020. (ISBN 978-7-121-39077-7) 周志华 著. 机器学习, 北京: 清华大学出版社, 2016. (ISBN 978-7-302-206853-6) 周志华, 杨强 主编. 机器学习及其应用 2011, 北京: 清华大学出版社, 2011. (ISBN 978-7-302-42328-7) [前言] 周志华, 王珏 主编. 机器学习及其应用 2009, 北京: 清华大学出版社, 2009. (ISBN 978-7-302-20419-0)
周志华, 王珏 主编. 机器学习及其应用 2007, 北京: 清华大学出版社, 2007. (ISBN 978-7-302-16076-2)
王珏, 周志华, 周傲英 主编. 机器学习及其应用, 北京: 清华大学出版社, 2006. (ISBN 7-302-12038-2)
周志华, 曹存根 主编. 神经网络及其应用, 北京: 清华大学出版社, 2004. (ISBN 7-302-08650-8) [前言] 张敏灵, 周志华. 多标记学习. , 见: 周志华, 杨强 主编, 机器学习及其应用 2011, 北京: 清华大学出版社, 2011, 179-199.
周志华, 张敏灵. MIML: 多示例多标记学习. 见: 周志华, 王珏 主编, 机器学习及其应用 2009, 北京: 清华大学出版社, 2009, 218-234 (第10章).
周志华. 半监督学习中的协同训练风范. 见: 周志华, 王珏 主编, 机器学习及其应用 2007, 北京: 清华大学出版社, 2007, 259-275 (第13章).
周志华. 多示例学习. 见: 刘大有 主编, 知识科学中的基本问题研究, 北京: 清华大学出版社, 2006, 322-336 (第12章).
周志华. 选择性集成. 见: 王珏, 周志华, 周傲英 主编, 机器学习及其应用, 北京: 清华大学出版社, 2006, 170-188 (第7章).
周志华. 神经网络规则抽取. 见: 周志华, 曹存根 主编, 神经网络及其应用, 北京: 清华大学出版社, 2004, 321-342 (第10章).
陈兆乾, 周志华, 陈世福. 神经计算研究现状及发展趋势. 见: 陆汝钤 主编, 知识科学与计算科学, 北京: 清华大学出版社, 2003, 165-193 (第8章).
周志华. 人工神经网络. 见: 陈世福, 陈兆乾 编著, 人工智能与知识工程, 南京: 南京大学出版社, 1997, 391-421 (第12章).
周志华. 关于强人工智能. 中国计算机学会通讯, 2018, 14(1): 45-46. 周志华. 机器学习: 发展与未来. 中国计算机学会通讯, 2017, 13(1): 44-51. 2016中国计算机大会特邀报告文章 周志华. 基于分歧的半监督学习. 自动化学报, 2013, 39(11): 1871-1878. 自动化学报创刊五十周年特邀综述 周志华. 机器学习与数据挖掘. 中国计算机学会通讯, 2007, 3(12): 35-44. 特邀综述 王璐, 姜远. K-近邻分类器鲁棒性验证: 从约束放松法到随机平滑法. 中国科学：信息科学, 2021, 51(1): 27-39.
冯霁, 蔡其志, 姜远. 联邦学习下对抗训练样本表示的研究. 中国科学：信息科学, 2021, 51(6): 900-911.
贺一笑, 庞明, 姜远. 蒙德里安深度森林. 计算机研究与发展, 2020, 57(8): 1594-1604.
任婕, 侯博建, 姜远. 多示例学习下的深度森林架构. 计算机研究与发展, 2019, 56(8): 1670-1676.
庞明, 周志华. 无组织恶意攻击检测问题的研究. 中国科学：信息科学, 2018, 48(2): 177-186.
吴西竹, 周志华. 领域知识指导的模型重用. 中国科学：信息科学, 2017, 47(11): 1482-1492.
