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Year: [2018] {2017 |2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 }

2018

[Conference Paper][Journal Article]

Conference Paper
  • L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou. Dynamic regret of strongly adaptive methods. In: Proceedings of the 35th International Conference on Machine Learning (ICML'18), Stockholm, Sweden, 2018.

  • H.-J. Ye, D.-C. Zhan, Y. Jiang, Z.-H. Zhou. Rectify Heterogeneous Model with Semantic Mapping. In: Proceedings of the 35th International Conference on Machine Learning (ICML'18), Stockholm, Sweden, 2018.

  • K. M. Ting, Y. Zhu, and Z.-H. 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.

  • Y. Yang, Y.-F. Wu, D.-C. Zhan, Z.-B. Liu, Y. Jiang. Complex Object Classification: A Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. In: Proceedings of the Annual Conference on ACM SIGKDD (KDD'18), London, UK, 2018.

  • S.-Y. Chen, Y. Yu, Q. Da, J. Tan, H.-K. Huang and H.-H. Tang. Stablizing reinforcement learning in dynamic environment with application to online recommendation. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18) (Research Track), London, UK, 2018.

  • Y.-J. Hu, Q. Da, A.-X. Zeng, Y. Yu and Y.-H. Xu. Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18) (Applied Track), London, UK, 2018.

  • C. Qian, C. Bian, Y. Yu, K. Tang, and X. Yao. Analysis of noisy evolutionary optimization when sampling fails. In: Proceedings of the 20th ACM Conference on Genetic and Evolutionary Computation (GECCO'18), Kyoto, Japan, 2018.

  • T. Zhang and Z.-H. Zhou. Semi-supervised optimal margin distribution machines. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

  • D.-D. Chen, W. Wang, W. Gao, and Z.-H. Zhou. Tri-net for semi-supervised deep learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

  • C. Zhang, Y. Yu, and Z.-H. Zhou. Learning environmental calibration actions for policy self-evolution. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

  • Y.-Q. Hu, Y. Yu, and Z.-H. Zhou. Experienced optimization with reusable directional model for hyper-parameter search. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

  • H.-H. Wei and M. Li. Positive and unlabeled learning for detecting software functional clones with adversarial training. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

  • Z. Xie and M. Li. Cutting the Software Building Efforts in Continuous Integration by Semi-Supervised Online AUC Optimization. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

  • H.-J. Ye, X.-R. Sheng, D.-C. Zhan, P. He. Distance Metric Facilitated Transportation between Heterogeneous Domains. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

  • Y. Yang, D.-C. Zhan, X.-R. Sheng, Y. Jiang. Semi-Supervised Multi-Modal Learning with Incomplete Modalities. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

  • Y. Yu, W.-J. Zhou. Mixture of GANs for clustering. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

  • B.-B. Gao, H.-Y. Zhou, J.-X. Wu, X. Geng. Age Estimation Using Expectation of Label Distribution Learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

  • C. Qian, Y. Yu, K. Tang. Approximation guarantees of stochastic greedy algorithms for subset selection. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

  • T. Wei, Y.-F. Li. Does tail label help for large-scale multi-label learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

  • D.-M. Liang, Y.-F. Li. Lightweight label propagation for large-scale network data. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

  • J. Feng and Z.-H. Zhou. AutoEncoder by forest. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • T. Zhang and Z.-H. Zhou. Optimal margin distribution clustering. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • P. Zhao and Z.-H. Zhou. Label distribution learning by optimal transport. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • H.-C. Dong, Y.-F. Li, and Z.-H. Zhou. Learning from semi-supervised weak-label data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • C. Liu, P. Zhao, S.-J. Huang, Y. Jiang, and Z.-H. Zhou. Dual set multi-label learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • H. Wang, H. Qian, and Y. Yu. Noisy derivative-free optimization with value suppression. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18) , New Orleans, LA, 2018.

  • Z. Xie and M. Li. Semi-supervised AUC optimization without guessing labels of unlabeled data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • Q.-Y. Jiang and W.-J. Li. Asymmetric Deep Supervised Hashing. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • L.-Z. Guo, Y.-F. Li. A general formulation for safely exploiting weakly supervised data. In: Proceedings of the 32nd AAAI conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • W.-Y. Lin, Y. Mi, J.-X. Wu, K.Lu and H.-K. Xiong. Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

  • Y. Yang, Y.-F. Wu, D.-C. Zhan, Y. Jiang. Multi-Network User Identification via Graph-Aware Embedding. In: Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18) , Melbourne, Australia, 2018.



Top

Journal Article and Book
  • H.-J. Ye, D.-C. Zhan, Y. Jiang, Z.-H. Zhou. What Makes Objects Similar: A Unified Multi-Metric Learning Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI:10.1109/TPAMI.2018. 2829192.

  • X.-Y. Guo and W. Wang. Towards making co-training suffer less from insufficient views. Frontiers of Computer Science, in press.

  • Y. Zhu, K. M. Ting, and Z.-H. Zhou. Multi-label learning with emerging new labels. IEEE Transactions on Knowledge and Data Engineering, in press.

  • Y. Zhu, J. Kwok, and Z.-H. Zhou. Multi-label learning with global and local correlation. IEEE Transactions on Knowledge and Data Engineering, in press.

  • C. Hou and Z.-H. Zhou. One-pass learning with incremental and decremental features. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.

  • Y. Yu, S.-Y. Chen, Q. Da, and Z.-H. Zhou. Reusable reinforcement learning via shallow trails. IEEE Transactions on Neural Networks and Learning Systems, in press.

  • Y.-X. Ding and Z.-H. Zhou. Crowdsourcing with unsure option. Machine Learning, in press.

  • T. Wei, L.-Z. Guo, Y.-F. Li, We. Gao. Learning safe multi-label prediction for weakly labeled data. Machine Learning. 107(4): 703-725, 2018.

  • H. Wang, S.-B. Wang, Y.-F. Li. Instance selection method for improving graph-based semi-supervised learning. Frontiers of Computer Science. In press.

  • C. Qian, J.-C. Shi, K. Tang, and Z.-H. Zhou. Constrained monotone k-submodular function maximization using multi-objective evolutionary algorithms with theoretical guarantee. IEEE Transactions on Evolutionary Computation, in press.

  • X.-S. Wei, C.-L. Zhang, H. Zhang, J.-X. Wu. Deep Bimodal Regression of Apparent Personality Traits from Short Video Sequences. IEEE Transactions on Affective Computing.

  • X.-S Wei, C.-W Xie, J.-X Wu, and C.-H Shen. Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recognition , 76, 2018: 704-714.

  • C. Qian, Y. Yu, and Z.-H. Zhou. Analyzing evolutionary optimization in noisy environments. Evolutionary Computation , 2018, in press.

  • C. Qian, Y. Yu, K. Tang, Y.-C Jin, X. Yao, and Z.-H. Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. Evolutionary Computation , 2018, in press.

  • T. Sun and Z.-H. Zhou. Structural diversity of decision tree ensemble learning. Frontiers of Computer Science, in press.

  • Z.-H. Zhou. A brief introduction to weakly supervised learning. National Science Review, 2018, 5(1): 44-53.









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