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论著

Year: [2020] {2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 }

2020

[Conference Paper][Journal Article]

Conference Paper

  • F.-Y. Liu, Z.-N. Liu, and C. Qian. Self-Guided Evolution Strategies with Historical Estimated Gradients. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), Yokohama, Japan, 2020.

  • C. Qian, H. Xiong, and K. Xue. Bayesian Optimization using Pseudo-Points. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), Yokohama, Japan, 2020.

  • S.-Q. Zhang and Z.-H. Zhou. Harmonic recurrent process for time series forecasting. In: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI'20), Santiago de Compostela, Spain, 2020.

  • L. Yang, X.-Z. Wu, Y. Jiang, and Z.-H. Zhou. Multi-label deep forest. In: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI'20), Santiago de Compostela, Spain, 2020.

  • Y.-Q. Hu, Z.-L. Liu, H. Yang, Y. Yu, and Y.-F. Liu. Derivative-free optimization with adaptive experience for efficient hyper-parameter tuning. In: Proceedings of the 24th European Conference on Artificial Intelligence (ECAI'20), Santiago de Compostela, Spain, 2020.

  • J.-H. Luo, J.-X. Wu. Neural Network Pruning with Residual-Connections and Limited-Data. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, USA, 2020.

  • C.-L. Zhang, Y.-H. Cao, J.-X. Wu. Rethinking the Route Towards Weakly Supervised Object Localization. In: Proceeding of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2020), Seattle, WA, 2020.

  • H.-J. Ye, H.-X. Hu, D.-C. Zhan, F. Sha. Few-Shot Learning via Embedding Adaptation with Set-to-Set Functions. In: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, WA, 2020.

  • H.-J. Ye, S. Lu, D.-C. Zhan. Distilling Cross-Task Knowledge via Relationship Matching. In: Proceedings of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, WA, 2020.

  • J.-Q. Yang, D.-C. Zhan, X.-C. Li. Bottom-Up and Top-Down Graph Pooling. In: Proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'20), Singapore, 2020.

  • X.-C. Li, D.-C. Zhan, J.-Q. Yang, Y. Shi, C. Hang, Y. Lu. Towards Understanding Transfer Learning Algorithms Using Meta Transfer Features. In: Proceedings of the 24th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'20), Singapore, 2020.

  • J. Wang and Z.-H. Zhou. Differentially private learning with small public data. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

  • X. Huo, M. Li, and Z.-H. Zhou. Control flow graph embedding based on multi-instance decomposition for bug localization. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

  • D. Xu, W.-J. Li. Hashing based Answer Selection. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI), New York, NY, 2020.

  • L.-Z. Guo, F. Kuang, Z.-X. Liu, Y.-F. Li, N. Ma, X.-H. Qie. Weakly-Supervised Learning Meets Ride-Sharing User Experience Enhancement. In: Proceedings of the 34th AAAI conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

  • Y.-N. Zhu, Y.-F. Li. Semi-Supervised Streaming Learning with Emerging New Labels. In: Proceedings of the 34th AAAI conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

  • Q.-W. Wang, L. Yang, Y.-F. Li. Learning from Weak-Label Data: A Deep Forest Expedition. In: Proceedings of the 34th AAAI conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

  • G. Wang, S. Lu, Y. Hu, and L.-J. Zhang. Adapting to Smoothness: A More Universal Algorithm for Online Convex Optimization. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, NY, 2020.

  • C. Qian, C. Bian, and C. Feng. Subset Selection by Pareto Optimization with Recombination. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

  • C. Bian, C. Feng, C. Qian, and Y. Yu. An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020.

  • G.-H. Wang, J.-X Wu. Repetitive Reprediction Deep Decipher for Semi-Supervised Learning. In: Proc. the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, NY, 2020.

  • Y. Yao, J.-H. Deng, X.-H. Chen, C. Gong, J.-X. Wu, J. Yang. Deep Discriminative CNN with Temporal Ensembling for Ambiguously-Labeled Image Classification. In: Proc. the 34th AAAI Conference on Artificial Intelligence (AAAI 2020), New York, NY, 2020.

  • Y.-M. Wang, R.-J. Song, X.-S. Wei, and L.-J. Zhang. An Adversarial Domain Adaptation Network for Cross-Domain Fine-Grained Recognition. In: Proceedings of the 2019 IEEE Winter Conference on Applications of Computer Vision (WACV 2020), to appear, 2020.

  • G. Wang, S. Lu, W. Tu, and L.-J. Zhang. SAdam: A Variant of Adam for Strongly Convex Functions. In: Proceedings of the 8th International Conference on Learning Representations (ICLR 2020), to appear, 2020.

  • L. Zhang, S. Lu, and T. Yang. Minimizing Dynamic Regret and Adaptive Regret Simultaneously. In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), to appear, 2020.

  • P. Zhao, L.-J. Zhang, and Z.-H. Zhou. A Simple Approach for Non-stationary Linear Bandits. In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), to appear, 2020.

  • P. Zhao, G. Wang, L.-J. Zhang, and Z.-H. Zhou. Bandit Convex Optimization in Non-stationary Environments. In: Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020), to appear, 2020.

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Journal Article and Book

  • H.-J. Ye, D.-C. Zhan, Y. Jiang, and Z.-H. Zhou. Heterogeneous few-shot model rectification with semantic mapping. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.

  • B.-J. Hou and Z.-H. Zhou. Learning with interpretable structure from gated RNN. In: IEEE Transactions on Neural Networks and Learning Systems, in press.

  • B.-J. Hou, L. Zhang, and Z.-H. Zhou. Learning with Feature Evolvable Streams. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2020.

  • Y. Wan, and L. Zhang. Accelerating Adaptive Online Learning by Matrix Approximation. In: International Journal of Data Science and Analytics (JDSA), in press, 2020.

  • 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), 32(1): 188 - 202, 2020.

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