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Pub_2020

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

  • 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

  • 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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