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论著

Year: [2019] {2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 }

2019

[Conference Paper][Journal Article]

Conference Paper
  • Q.-Y. Jiang, Y. He, G. Li, J. Lin, L. Li, W.-J. Li. SVD: A Large-Scale Short Video Dataset for Near Duplicate Video Retrieval. In: Proceedings of International Conference on Computer Vision (ICCV), 2019.

  • W.-Z. Dai, Q. Xu, Y. Yu, and Z.-H. Zhou. Bridging machine learning and logical reasoning by abductive learning. In: Advances in Neural Information Processing Systems 32 (NeurIPS'19) (Vancouver, Canada), 2019.

  • S.-H. Lv, L. Yang, and Z.-H. Zhou. A refined margin distribution analysis for forest representation learning. In: Advances in Neural Information Processing Systems 32 (NeurIPS'19) (Vancouver, Canada), 2019.

  • J. Feng, Q.-Z. Cai, and Z.-H. Zhou. Learning to confuse: Generating training time adversarial data with auto-encoder. In: Advances in Neural Information Processing Systems 32 (NeurIPS'19) (Vancouver, Canada), 2019.

  • L. Fan, Q.-Y. Jiang, Y.-Q. Yu, W.-J. Li. Deep Hashing for Speaker Identification and Retrieval. In: Proceedings of Proceedings of the Annual Conference of the International Speech Communication Association (INTERSPEECH), 2019.

  • G.-H. Wang, S. Lu and L. Zhang. Adaptivity and Optimality: A Universal Algorithm for Online Convex Optimization. In: Proceedings of the 35th Conference on Uncertainty in Artificial Intelligence (UAI 2019), 2019, to appear.

  • S.-Y. Lu, G.-H. Wang, Y. Hu, L. Zhang. Multi-Objective Generalized Linear Bandits. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, 2019, to appear.

  • P. Li, J. Yi, B. Zhou, L. Zhang. Improving the Robustness of Deep Neural Networks via Adversarial Training with Triplet Loss. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, 2019, to appear.

  • Y. Yang, K.-T. Wang, D.-C. Zhan, H. Xiong, Y. Jiang. Comprehensive Semi-Supervised Multi-Modal Learning. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, 2019.

  • Z.-Y. Fu, D.-C. Zhan, X.-C. Li, Y.-X. Lu. Automatic Successive Reinforcement Learning with Multiple Auxiliary Rewards. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, 2019.

  • Y.-Q. Hu, Y. Yu, J.-D. Liao. Cascaded algorithm-selection and hyper-parameter optimization with extreme-region upper confidence bound bandit. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, 2019.

  • W.-J Zhou, Y. Yu, Y.-F. Chen, K. Guan, T.-J. Lv, C.-J. F, Z.-H. 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.

  • F. Shi, Y.-F. Li. Rapid Performance Improvement through Active Model Reuse. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macau, China, 2019.

  • T. Wei, W.-W. Tu, Y.-F. Li. Learning for Tail Label Data: A Label-Specific Feature Approach. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macau, China, 2019.

  • Q.-W. Wang, Y.-F. Li, Z.-H. Zhou. Partial Label Learning with Unlabeled Data. In: Proceedings of the 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macau, China, 2019.

  • L. Zhang, Z.-H. Zhou. Stochastic Approximation of Smooth and Strongly Convex Functions: Beyond the O(1/T) Convergence Rate. In: Proceedings of the 32nd Annual Conference on Learning Theory (COLT 2019), Phoenix, AZ, 2019, to appear.

  • X.-Z. Wu, S. Liu, and Z.-H. Zhou. Heterogeneous model reuse via optimizing multiparty multiclass margin. In: Proceedings of the 36th International Conference on Machine Learning (ICML'19), Long Beach, CA, 2019, pp.6840-6849.

  • L. Zhang, T.-Y. Liu, Z.-H. Zhou. Adaptive Regret of Convex and Smooth Functions. In: Proceedings of the 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, pages 7414 - 7423, 2019.

  • S.-Y. Lu, G.-H. Wang, Y. Hu, L. Zhang. Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards. In: Proceedings of the 36th International Conference on Machine Learning (ICML 2019), Long Beach, CA, pages 4154 - 4163, 2019.

  • B.-C. Xu, K. M. Ting, and Z.-H. Zhou. Isolation set-kernel and its application to multi-instance learning. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), Anchorage, AL, 2019.

  • Z.-Y. Zhang, P. Zhao, Y. Jiang, and Z.-H. Zhou. Learning from incomplete and inaccurate supervision. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), Anchorage, AL., 2019.

  • W.-J. Shang, Y. Yu, Q.-Y. Li, Z.-W. Qin, Y.-P. Meng, J.-P. Ye. Environment reconstruction with hidden confounders for reinforcement learning based recommendation. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), Anchorage, AL, 2019.

  • Y. Yang, D.-W. Zhou, D.-C. Zhan, H. Xiong, Y. Jiang. Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability. In: Proceedings of the 25th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'19), Anchorage, AL., 2019.

  • T.-Z. Wang, S.-J. Huang, Z.-H. Zhou.. Towards identifying causal relation between instances and labels.. In: Proceedings of the 19th SIAM International Conference on Data Mining (SDM'19), Calgary, Canada, 2019.

  • Y.-Q. Yu, L. Fan, W.-J. Li. Ensemble Additive Margin Softmax for Speaker Verification. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2019.

  • K. Yi, J.-X. Wu. Probabilistic End-to-end Noise Correction for Learning with Noisy Labels. In: Proceedings of the IEEE Int'l Conference on Computer Vision and Pattern Recognition (CVPR 2019), Long Beach, CA, USA, 19-Jun.

  • L.-Z. Guo, T. Han, Y.-F. Li. Robust Semi-Supervised Representation Learning for Graph-Structured Data. In: Proceedings of the 23rd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'19), Macau, China, 2019.

  • Y.-F. Li, H. Wang, T. Wei, W.-W. Tu. Towards Automated Semi-Supervised Learning. In: Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • T. Wei, Y.-F. Li. Learning compact model for large-scale multi-label learning. In: Proceedings of the 33rd AAAI conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • J.-C. Shi, Y. Yu, Q. Da, S.-Y. Chen, A.-X. Zeng. Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • Y.-Q. Hu, Y. Yu, W.-W. Tu, Q. Yang, Y.-Q. Chen, W.-Y. Dai. Multi-fidelity automatic hyper-parameter tuning via transfer series expansion. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • Z.-J. Pang, R.-Z. Liu, Z.-Y. Meng, Y. Zhang, Y. Yu, T. Lu. On reinforcement learning for full-length game of StarCraft. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • T. R. R. S. Lu, S. L.-A. Jiang. Multi-View Anomaly Detection: Neighborhood in Locality Matters. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • Y. Yang, Y.-F. Wu, D.-C. Zhan, Z.-B. Liu, Y. Jiang. Deep Robust Unsupervised Multi-Modal Network. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • Y.-Y. Zhang and M. Li. Find me if you can: Deep software clone detection by exploiting the contest between the plagiarist and the detector. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • S.-T. Shi, M. Li, D. Lo, F. Thung, and X. Huo. Automatic code review by learning the revision of source code. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • Z.-H. Tan, T. Zhang and W. Wang. Coreset Stochastic Variance-Reduced Gradient with Application to Optimal Margin Distribution Machine. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

  • B.-B. Yang and W. Gao. Weighted Oblique Decision Trees. In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019.

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Journal Article and Book
  • T. Wei, Y.-F. Li. Does Tail Label Help for Large-Scale Multi-Label Learning. In: IEEE Transactions on Neural Network and Learning Systems (TNNLS), In press.

  • C. Qian, Y. Yu, K. Tang, X. Yao, Z.-H. Zhou. Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms. In: Artificial Intelligence, 2019, 275: 279-294.

  • Y. Wan, and L. Zhang. Accelerating Adaptive Online Learning by Matrix Approximation. In: International Journal of Data Science and Analytics (JDSA), in press, 2019.

  • X.-S. Wei, P. Wang, L.-Q. Liu, C.-H. Shen, J.-X. Wu. Piecewise Classifier Mappings: Learning Fine-grained Learners for Novel Categories with Few Examples. IEEE Transactions on Image Processing, 28(12), 2019: 6116-6125.

  • Z.-H. Tan, P. Tan, Y. Jiang, and Z.-H. Zhou. Multi-label optimal margin distribution machine. In: Machine Learning, in press.

  • P. Zhao, L.-W. Cai, and Z.-H. Zhou. Handling concept drift via model reuse. In: Machine Learning, in press.

  • T. Yang, L. Zhang, R. Jin, S. Zhu, and Z.-H. Zhou. A Simple Homotopy Proximal Mapping Algorithm for Compressive Sensing. Machine Learning, 108(6): 1019 - 1056, 2019.

  • H.-J. Ye, D.-C. Zhan, N. Li, Y. Jiang. Learning Multiple Local Metrics: Global Consideration Helps. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2019, in press.

  • Y.-F. Li, L.-Z. Guo (co-first author), Z.-H. Zhou. Towards Safe Weakly Supervised Learning. IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), in press.

  • Y. Yang, D.-C. Zhan, Y.-F. Wu, Z.-B. Liu, H. Xiong, Y. Jiang. Semi-Supervised Multi-Modal Clustering and Classification with Incomplete Modalities. In: IEEE Transactions on Knowledge and Data Engineering, Accepted.

  • Y. Yang, Z.-Y. Fu, D.-C. Zhan, Z.-B. Liu, Y. Jiang. Semi-Supervised Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. In: IEEE Transactions on Knowledge and Data Engineering, Accepted.

  • Y.-F. Li, D.-M. Liang (co-first author). Lightweight Label Propagation for Large-Scale Network Data. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press.

  • X.-S. Wei, H.-J. Ye, J.-X. Wu, C.-H. Shen, Z.-H. Zhou. Multi-Instance Learning with Emerging Novel Class. In: IEEE Transactions on Knowledge and Data Engineering, accepted for publication, to appear.

  • X.-S. Wei, H.-J. Ye, X. Mu, J. Wu, C. Shen, and Z.-H. Zhou. Multi-instance learning with emerging novel class. In: IEEE Transactions on Knowledge and Data Engineering, in press.

  • F. Shang, K. Zhou, H. Liu, J. Cheng, I. W. Tsang, L. Zhang, D. Tao, and L. Jiao. VR-SGD: A Simple Stochastic Variance Reduction Method for Machine Learning. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2019.

  • B.-J. Hou, L. Zhang, and Z.-H. Zhou. Learning with feature evolvable streams. In: IEEE Transactions on Knowledge and Data Engineering, in press.

  • M. Xu, Y.-F. Li, Z.-H. Zhou. Robust Multi-Label Learning with PRO Loss. In:IEEE Transactions on Knowledge and Data Engineering (TKDE), in press.

  • W.-H. zheng, H.-Y. Zhou, M. Li, J.-X. Wu. CodeAttention: Translating Source Code to Comments by Exploiting the Code Constructs. Frontiers of Computer Science (FCS), 13(3), 2019: 565-578.

  • Y.-F. Li, D.-M. Liang. Safe Semi-Supervised Learning: A Brief Introduction. Frontiers of Computer Science (FCS), in press.

  • Q.-Y. Jiang, W.-J. Li. Discrete Latent Factor Model for Cross-Modal Hashing. IEEE Transactions on Image Processing (TIP), 2019.

  • L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou. Analysis of Nuclear Norm Regularization for Full-rank Matrix Completion. In: Journal of Machine Learning Research (JMLR), 20(97):1 - 22, 2019.

  • L. Luo, C. Chen, Z.-H. Zhang, M. Li, J.-X. Wu. Robust Frequent Directions with Application in Online Learning. Journal of Machine Learning Research (JMLR), 2019.

  • J.-H. Luo, H. Zhang, J.-X. Wu, C.-H. Shen, Z.-H. Zhou. ThiNet: Pruning CNN Filters for a Thinner Net. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2019.

  • C.-L. Zhang, J.-X. Wu. Improving CNN Linear Layers with Power Mean Non-Linearity. Pattern Recognition, 89, 12-21, 2019.

  • X.-S. Wei, C.-L. Zhang, J.-X. Wu, C.-H. Shen, Z.-H. Zhou. Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transformation. Pattern Recognition, 88, 113-126,, 2019.

  • X.-S. Wei, C.-L. Zhang, J.-X. Wu, C.-H. Shen, Z.-H. Zhou. Unsupervised Object Discovery and Co-Localization by Deep Descriptor Transformation. Pattern Recognition, 88, 113-126, 2019.

  • C.-L. Zhang, J.-X. Wu. Improving CNN Linear Layers with Power Mean Non-Linearity. Pattern Recognition, 89, 12-21, 2019.
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