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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

[AAAI] [AISTATS] [CVPR] [ECAI] [ECCV] [ICLR] [ICML] [IJCAI] [MICCAI] [KDD] [PAKDD] [UAI] [WACV]

AAAI

• C. Bian, C. Feng, C. Qian, 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.

• C. Qian, C. Bian, 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.

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

• G.-H. Wang, J.-X. Wu. Repetitive Reprediction Deep Decipher for Semi-Supervised Learning. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, USA, 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.

• 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.

• 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.

• 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.

• S. Li, W.-T. Li, W. Wang. Co-GCN for Multi-View Semi-Supervised Learning. In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York City, 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.

• 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: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, USA, 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.

AISTATS

• 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.

CVPR

• C.-L. Zhang, Y.-H. Cao, J.-X. Wu. Rethinking the Route Towards Weakly Supervised Object Localization. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, WA, USA, 2020.

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

• H.-J. Ye, H.-X. Hu, D.-C. Zhan, F. Sha. Learning Embedding Adaptation for Few-Shot Learning. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, Washington, 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, Washington, 2020.

• J.-H. Luo, J.-X. Wu. Neural Network Pruning with Residual-Connections and Limited-Data. In: Proceedings of the IEEE International Conference on Computer Vision and Pattern Recognition (CVPR'20), Seattle, WA, USA, 2020.

ECAI

• 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.

• 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.

• 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.

ECCV

• Q. Cui, Q.-Y. Jiang, X.-S. Wei, W.-J. Li, Osamu Yoshie. ExchNet: A Unified Hashing Network for Accelerating Fine-Grained Image Retrieval. In: Proceedings of the European Conference on Computer Vision (ECCV'20), 2020.

ICLR

• 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.

ICML

• L.-Z. Guo, Z.-Y. Zhang, Y. Jiang, Y.-F. Li, and Z.-H. Zhou. Safe deep semi-supervised learning for unseen-class unlabeled data. In: Proceedings of the 37th International Conference on Machine Learning (ICML'20), 2020.

• T.-Z. Wang, X.-Z. Wu, S.-J. Huang, and Z.-H. Zhou. Cost-effectively identifying causal effect when only response variable observable. In: Proceedings of the 37th International Conference on Machine Learning (ICML'20), 2020.

• Y. Wan, W.-W. Tu, L.-J. Zhang. Projection-free Distributed Online Convex Optimization with $O(\sqrt{T})$ Communication Complexity. In: Proceedings of the 37th International Conference on Machine Learning (ICML'20), 2020.

• Y. Yan, Y. Xu, L.-J. Zhang, X. Wang, T. Yang. Stochastic Optimization for Non-convex Inf-Projection Problems. In: Proceedings of the 37th International Conference on Machine Learning (ICML'20), 2020.

• Z.-Y. Zhang, P. Zhao, Y. Jiang, and Z.-H. Zhou. Learning with feature and distribution evolvable streams. In: Proceedings of the 37th International Conference on Machine Learning (ICML'20), 2020.

IJCAI

• B. Xue, G. Wang, Y. Wang, and L.-J. Zhang. Nearly Optimal Regret for Stochastic Linear Bandits with Heavy-Tailed Payoffs. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), 2020.

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

• F.-Y. Liu, Z.-N. Li, 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.

• L.-J. Zhang. Online Learning in Changing Environments. In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), 2020.

MICCAI

• K. Fang, W.-J. Li. DMNet: Difference Minimization Network for Semi-supervised Segmentation in Medical Images. In: Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI'20), 2020.

KDD

• K. M. Ting, B.-C. Xu, T. Washio, and Z.-H. Zhou. Isolation distribution kernel: A new tool for kernel based anomaly detection. In: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'20), 2020.

• L.-Z. Guo, Z. Zhou, Y.-F. Li. RECORD: Resource Constrained Semi-Supervised Learning under Distribution Shift. In: Proceedings of the 26th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'20), San Diego, CA, 2020.

PAKDD

• 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.

UAI

• Y.-J. Zhang, P. Zhao, and Z.-H. Zhou. A simple online algorithm for competing with dynamic comparators. In: Proceedings of the 36th Conference on Uncertainty in Artificial Intelligence (UAI'20), Toronto, Canada, 2020.

WACV

• 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.

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Journal Article and Book

[A] [I] [M] [P] [S] [T]

Algorithmica

• C. Qian, C. Bian, Y. Yu, K. Tang, and X. Yao. Analysis of Noisy Evolutionary Optimization When Sampling Fails. In: Algorithmica.

IEEE Transactions on Cybernetics

• F. Xiong, Y. Xiao, Z.-G. Cao, Y.-C. Wang, Joey Tianyi Zhou, J.-X. Wu. ECML: An Ensemble Cascade Metric Learning Mechanism towards Face Verification. In: IEEE Transactions on Cybernetics.

• W.-J. Hong, C. Qian, and K. Tang. Efficient Minimum Cost Seed Selection with Theoretical Guarantees for Competitive Influence Maximization. In: IEEE Transactions on Cybernetics.

IEEE Transactions on Knowledge and Data Engineering (TKDE)

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

• M. Xu, Y.-F. Li, and Z.-H. Zhou. Robust multi-label learning with PRO loss. In: IEEE Transactions on Knowledge and Data Engineering, 2020.

• P. Zhao, X. Wang, S. Xie, L. Guo, and Z.-H. Zhou. Distribution-free one-pass learning. In: IEEE Transactions on Knowledge and Data Engineering.

• T. Zhang and Z.-H. Zhou. Optimal margin distribution machine. In: IEEE Transactions on Knowledge and Data Engineering, 2020.

• 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.

IEEE Transactions on Neural Networks and Learning Systems

• 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.

IEEE Transactions on Pattern Analysis and Machine Intelligence

• 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.

• Y.-F. Li, L.-Z. Guo, and Z.-H. Zhou. Towards safe weakly supervised learning. In: IEEE Transactions on Pattern Analysis and Machine Intelligence.

International Journal of Data Science and Analytics (JDSA)

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

Machine Learning

• P. Zhao, L.-W. Cai, and Z.-H. Zhou. Handling concept drift via model reuse. In: Machine Learning, 2020.

• Z.-H. Tan, P. Tan, Y. Jiang, and Z.-H. Zhou. Multi-label optimal margin distribution machine. In: Machine Learning, 2020.

Pattern Recognition

• J.-H. Luo, J.-X. Wu. AutoPruner: An End-to-End Trainable Filter Pruning Method for Efficient Deep Model Inference. In: Pattern Recognition.

Science China Information Sciences

• Z.-H. Zhou. Why over-parameterization of deep neural networks does not overfit?. In: Science China Information Sciences.

Theoretical Computer Science

• C. Bian, C. Qian, K. Tang, and Y. Yu. Running Time Analysis of the (1+1)-EA for Robust Linear Optimization. In: Theoretical Computer Science.

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