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Pub_2021

Year: [2021] {2020 | 2019 | 2018 | 2017 | 2016 | 2015 | 2014 | 2013 | 2012 | 2011 | 2010 | 2009 | 2008 | 2007 | 2006 | 2005 | 2004 | 2003 }

2021

[Conference Paper][Journal Article]

Conference Paper

[AAAI] [AAMAS] [CEC] [CIKM] [CVPR] [ECML] [ICASSP] [ICCV] [ICDM] [ICIG] [ICLR] [ICML] [IJCAI] [IJCNN] [KDD] [L4DC] [MM] [NeurIPS] [PAKDD] [SDM]

AAAI

  • B.-J. Hou, Y.-H. Yan, P. Zhao, and Z.-H. Zhou. Storage Fit Learning with Feature Evolvable Streams. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • C. Feng and C. Qian. Multi-objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • H.-J. Ye, X.-C. Li, D.-C. Zhan. Task Cooperation for Semi-Supervised Few-Shot Learning. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • J.-Q. Yang, X. Li, S.-G. Han, T. Zhuang, D.-C. Zhan, X.-Y. Zeng, B. Tong. Capturing Delayed Feedback in Conversion Rate Prediction via Elapsed-Time Sampling. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • P. Zhao, Y.-J. Zhang, and Z.-H. Zhou. Exploratory Machine Learning with Unknown Unknowns. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • S. Li, W. Wang, W.-T. Li, P. Chen. Multi-View Representation Learning with Manifold Smoothness. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • S. Lu, G.-H. Wang, and L.-J. Zhang. Stochastic Graphical Bandits with Adversarial Corruptions. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • S. Lu, H.-J. Ye, D.-C. Zhan. Tailoring Embedding Function to Heterogeneous Few-Shot Tasks by Global and Local Feature Adaptors. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • S. Lu, Y. Hu, and L.-J. Zhang. Stochastic Bandits with Graph Feedback in Non-Stationary Environments. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • T. Han, W.-W. Tu, Y.-F. Li, Explanation Consistency Training: Facilitating Consistency-Based Semi-Supervised Learning with Interpretability. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • Y.-J. Zhang, Y.-H. Yan, P. Zhao, and Z.-H. Zhou. Towards Enabling Learnware to Handle Unseen Jobs. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • Y.-S. Zhang, X.-S. Wei, B.-Y. Zhou, J.-X. Wu. Bag of Tricks for Long-Tailed Visual Recognition with Deep Convolutional Neural Networks. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • Y.-Y. Wan, B. Xue, and L.-J. Zhang. Projection-Free Online Learning in Dynamic Environments. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • Y.-Y. Wan, and L.-J. Zhang. Approximate Multiplication of Sparse Matrices with Limited Space. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

  • Y.-Y. Wan, and L.-J. Zhang. Projection-free Online Learning over Strongly Convex Sets. In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI), 2021.

AAMAS

  • X.-Q. Cai, Y.-X. Ding, Y. Jiang, and Z.-H. Zhou. Imitation Learning from Pixel-Level Demonstrations by HashReward. In: Proceedings of the 20th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS), 2021.

CEC

  • F.-Y. Liu and C. Qian. Prediction Guided Meta-Learning for Multi-Objective Reinforcement Learning. In: Proceedings of the 2021 IEEE Congress on Evolutionary Computation (CEC), Krakow, Poland, 2021.

CIKM

  • Z.-H. Qiu, Y.-C. Jian, Q.-G. Chen, and L.-J. Zhang. Learning to Augment Imbalanced Data for Re-ranking Models. In: Proceedings of the 30th ACM International Conference on Information and Knowledge Management (CIKM 2021), pages 1478 - 1487, 2021.

CVPR

  • D.-W. Zhou, H.-J. Ye, D.-C. Zhan. Learning Placeholders for Open-Set Recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2021.

ECML

  • X.-C. Li, D.-C. Zhan, Y.-F. Shao, B.-S. Li, S.-M. Song. FedPHP: Federated Personalization with Inherited Private Models. In: Proceedings of the 2021 European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML/PKDD), 2021.

ICASSP

  • Y.-Q. Yu, S.-Q. Zheng, H.-B. Suo, Y. Lei, W.-J. Li. CAM: Context-Aware Masking for Robust Speaker Verification. In: Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2021.

ICCV

  • H.-J. Ye, D.-C. Zhan, W.-L. Chao. Procrustean Training for Imbalanced Deep Learning. In: Proceedings of the International Conference on Computer Vision (ICCV), 2021.

  • K. Zhu, and J.-X. Wu. Residual Attention: A Simple but Effective Method for Multi-Label Recognition. In: Proceedings of the International Conference on Computer Vision (ICCV), 2021.

  • Y.-Y. He, J.-X. Wu, and X.-S. Wei. Distilling Virtual Examples for Long-tailed Recognition. In: Proceedings of the International Conference on Computer Vision (ICCV), 2021.

  • Z.-F. Wu, T. Wei, J.-W. Jiang, C.-J. Mao, M.-Q. Tang, Y.-F. Li. NGC: A Unified Framework for Learning with Open-World Noisy Data. In: Proceedings of the International Conference on Computer Vision (ICCV), 2021.

  • Z.-R. Sun, Y.-Z. Yao, X.-S. Wei, Y.-S. Zhang, F.-M. Shen, J.-X. Wu, J. Zhang, and H.-T. Shen. Webly Supervised Fine-Grained Recognition: Benchmark Datasets and An Approach. In: Proceedings of the International Conference on Computer Vision (ICCV), 2021.

ICDM

  • Y.-H. Chen, S.-H. Lyu, and Y. Jiang. Improving Deep Forest by Exploiting High-order Interactions. In: Proceedings of the 21th IEEE International Conference on Data Mining (ICDM), 2021.

  • Z.-Y. Zhang, S.-Q. Zhang, Y. Jiang, and Z.-H. Zhou. LIFE: Learning Individual Features for Multivariate Time Series Prediction with Missing Values. In: Proceedings of the 21th IEEE International Conference on Data Mining (ICDM), 2021.

ICIG

  • H. Yu, H.-Y. Wang, and J.-X. Wu. Mixup without Hesitation. In: Proceedings of the 11th International Conference on Image and Graphics (ICIG), 2021.

ICLR

  • J.-H. Wang, Z.-Z. Ren, T. Liu, Y. Yu, and C.-J. Zhang. QPLEX: Duplex dueling multi-agent Q-Learning. In: Proceedings of the 9th International Conference on Learning Representations (ICLR), 2021.

ICML

  • T. Qin, T.-Z. Wang, and Z.-H. Zhou. Budgeted Heterogeneous Treatment Effect Estimation. In: Proceedings of the 38th International Conference on Machine Learning (ICML), 2021.

  • Y.-R. Yang, and W.-J. Li. BASGD: Buffered Asynchronous SGD for Byzantine Learning. In: Proceedings of the International Conference on Machine Learning (ICML), 2021.

IJCAI

  • C. Bian, C. Qian, F. Neumann, and Y. Yu. Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021.

  • J.-J. Shao, Z.-Z. Cheng, Y.-F. Li, S.-L. Pu. Towards Robust Model Reuse in the Presence of Latent Domains. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021.

  • K.-L. Yao, and W.-J. Li. Blocking-based Neighbor Sampling for Large-scale Graph Neural Networks. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021.

  • K. Xue, C. Qian, L. Xu, and X.-D. Fei. Evolutionary Gradient Descent for Non-convex Optimization. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021.

  • X.-J. Gui, W. Wang, and Z.-H. Tian. Towards Understanding Deep Learning from Noisy Labels with Small-Loss Criterion. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021.

  • Y.-M. Wang, B. Xue, Q. Cheng, Y.-H. Chen, and L.-J. Zhang. Deep Unified Cross-Modality Hashing by Pairwise Data Alignment. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021.

  • Y. Yang, C.-B. Zhang, Yi-Chu Xu, D,-H. Yu, D.-C. Zhan, and J. Yang. Rethinking Label-Wise Cross-Modal Retrieval from A Semantic Sharing Perspective. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), 2021.

IJCNN

  • C. Hang, W. Wang, and D.-C. Zhan. Multi-Modal Multi-Instance Multi-Label Learning with Graph Convolutional Network. In: Proceedings of 2020 International Joint Conference on Neural Networks (IJCNN), 2021.

KDD

  • J.-C. Wang, D.-Z. Deng, X. Xie, X.-H. Shu, Y.-X. Huang, L.-W. Cai, H. Zhang, M.-L. Zhang, Z.-H. Zhou, and Y.-C. Wu. Tac-Valuer: Knowledge-based Stroke Evaluation in Table Tennis. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • L.-Z. Guo, Z. Zhou, J.-J. Shao, Q. Zhang, F. Kuang, G.-L. Li, Z.-X. Liu, G.-B. Wu, Nan Ma, Q. Li, Y.-F. Li. Learning from Imbalanced and Incomplete Supervision with Its Application to Ride-Sharing Liability Judgment. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • T. Wei, J.-X. Shi, and Y.-F. Li. Probabilistic Label Tree for Streaming Multi-Label Learning. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • T. Wei, W.-W. Tu, Y.-F. Li, and G.-P. Yan. Towards Robust Prediction on Tail Labels. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • X.-C. Li, and D.-C. Zhan. FedRS: Federated Learning with Restricted Softmax for Label Distribution Non-IID Data. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

  • Y. Zhang, Y. Zhang, and W. Wang. Multi-Task Learning via Generalized Tensor Trace Norm. In: Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2021.

L4DC

  • P. Zhao, and L.-J. Zhang. Improved Analysis for Dynamic Regret of Strongly Convex and Smooth Functions. In: Proceedings of the 3rd Annual Learning for Dynamics and Control Conference (L4DC), pages 48 - 59, 2021.

MM

  • D.-W. Zhou, H.-J. Ye, D.-C. Zhan. Co-Transport for Class-Incremental Learning. In: Proceedings of the 29th ACM International Conference on Multimedia (MM), 2021.

  • Y.-S. Gong, J.-F. Yi, D.-D. Chen, J Zhang, J.-Y. Zhou, and Z.-H. Zhou. Inferring the Importance of Product Appearance with Semi-supervised Multi-modal Enhancement. In: Proceedings of the 29th ACM International Conference on Multimedia (MM), 2021.

NeurIPS

  • C.-Y. Wu, G.-Y. Yang, Z.-Z. Zhang, Y. Yu, D. Li, W.-L. Liu, J-.Y. Hao. Adaptive Online Packing-guided Search for POMDPs. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • G.-H. Wang, Y.-Y. Wan, T.-B. Yang, and L.-J. Zhang. Online Convex Optimization with Continuous Switching Constraint. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • L.-J. Zhang, G.-H. Wan, W.-W. Tu, and Z.-H. Zhou. Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • L.-J. Zhang, W. Jiang, S. Lu, and T.-B. Yang. Revisiting Smoothed Online Learning. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • S. Lu, H.-J. Ye, L. Gan, and D.-C. Zhan. Towards Enabling Meta-Learning from Target Models. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • T.-Z. Wang, and Z.-H. Zhou. Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • X.-H. Chen, S.-Y. Jiang, F. Xu, Z.-Z. Zhang, Y. Yu. Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • X.-H. Chen, Y. Yu, Q.-Y. Li, F.-M. Luo, Z.-W. Qin, W.-J. Shang, and J.-P. Ye. Offline Model-based Adaptable Policy Learning. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • X.-H. Liu, Z.-H. Xue, J.-C. Pang, S.-Y. Jiang, F. Xu, Y. Yu. Regret Minimization Experience Replay in Off-Policy Reinforcement Learning. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • Y.-X. Huang, W.-Z. Dai, L.-W. Cai, S. Muggleton, and Y. Jiang. Fast Abductive Learning by Similarity-based Consistency Optimization. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

  • Z. Zhou, L.-Z. Guo, Z.-Z. Cheng, Y.-F. Li, S.-L. Pu. STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data. In: Advances in Neural Information Processing Systems 34 (NeurIPS), 2021.

PAKDD

  • D.-W. Zhou, Y. Yang, D.-C. Zhan. Detecting Sequentially Novel Classes with Stable Generalization Ability. In: Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD), 2021.

SDM

  • Y.-X. Xu, M. Pang, J. Feng, K.-M. Ting, Y. Jiang, and Z.-H. Zhou. Reconstruction-based Anomaly Detection with Completely Random Forest. In: Proceedings of the 21st SIAM International Conference on Data Mining (SDM), 2021.

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Journal Article and Book

[A] [E] [I] [J] [M] [N] [P] [S] [] []

Algorithmica

  • C. Qian, C. Bian, Y. Yu, K. Tang, and X. Yao. Analysis of Noisy Evolutionary Optimization When Sampling Fails. In: Algorithmica, 2021, 83(4): 940-975.

Artificial Intelligence

  • W. Gao, T. Zhang, B.-B. Yang, and Z.-H. Zhou. On the Noise Estimation Statistics. In: Artificial Intelligence, 2021, 293: 103451.

Autonomous Agents and Multi-Agent Systems

  • Y. Zheng, J.-Y. Hao, Z.-Z. Zhang, Z.-P. Meng, T-.P. Yang, Y.-R. Li, and C.-J. Fan, Efficient Policy Detecting and Reusing for Non-Stationarity in Markov Games, In: Autonomous Agents and Multi-Agent Systems, 2021, 35(2): 1-29.

Evolutionary Computation

  • C. Qian. Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions. In: Evolutionary Computation, 2021, 29(4): 463–490

Formal Aspects of Computing

  • L. Bu, Y.-J. Liang, Z.-Y. Xie, H. Qian, Y.-Q. Hu, Y. Yu, X. Chen, and X.-D. Li. Machine learning steered symbolic execution framework for complex software code. In: Formal Aspects of Computing, 2021, 33(3): 301-323.

Frontiers of Computer Science

  • H- Qian, and Y- Yu. Derivative-free reinforcement learning: A review. In: Frontiers of Computer Science, 2021, 15(6): 156336.

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, 2021, 51(12): 6091-6104.

IEEE Transactions on Knowledge and Data Engineering

  • B.-J. Hou, L.-J. Zhang, and Z.-H. Zhou. Learning with feature evolvable streams. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021, 33(6): 2602-2615.

  • B.-J. Hou, L.-J. Zhang, and Z.-H. Zhou. Prediction with Unpredictable Feature Evolution. In: IEEE Transactions on Neural Networks and Learning Systems (TNNLS), in press, 2021.

  • D.-W. Zhou, Y. Yang, and D.-C. Zhan. Learning to Classify with Incremental New Class. In: IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2021

  • G. Huzhang, Z.-J. Pang, Y.-Q. Gao, Y.-W. Liu, W.-J. Shen, W.-J. Zhou, Q. Da, A.-X. Zeng, H. Yu, Y. Yu, and Z.-H. Zhou. AliExpress Learning-To-Rank: Maximizing Online Model Performance without Going Online. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press. 2021.

  • K.-M. Ting, B.-C. Xu, T. Washio, and Z.-H. Zhou. Isolation Distributional Kernel: A New Tool for Point and Group Anomaly Detections. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2021.

  • M. Pang, K.-M. Ting, P. Zhao, and Z.-H. Zhou. Improving Deep Forest by Screening. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2021.

  • P. Zhao, X.-Q. Wang, S.-Y. Xie, L. Guo, and Z.-H. Zhou. Distribution-free one-pass learning. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021, 33(3): 951-963.

  • X.-S. Wei, H.-J. Ye, X. Mu, J.-X. Wu, C.-H. Shen, and Z.-H. Zhou. Multi-instance learning with emerging novel class. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021, 33(5): 2109-2120.

  • X.-Z. Wu, W.-K. Xu, S. Liu, and Z.-H. Zhou. Model Reuse with Reduced Kernel Mean Embedding Specification. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2021.

  • Y. Yang, D.-W. Zhou, D.-C. Zhan, H. Xiong, Y. Jiang, and J. Yang. Cost-Effective Incremental Deep Model: Matching Model Capacity with the Least Sampling. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2021.

  • Y. Yang, J.-Q. Yang, R. Bao, D.-C. Zhan, H.-S. Zhu, X.-R. Gao, H. Xiong, and J. Yang. Corporate Relative Valuation using Heterogeneous Multi-Modal Graph Neural Network. In: IEEE Transactions on Knowledge and Data Engineering (TKDE).

  • Y.-F. Li, D.-M. Liang. Lightweight Label Propagation for Large-Scale Network Data. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), 2021, 33(5): 2071-2082.

  • Z.-Y. Zhang, P. Zhao, Y. Jiang, and Z.-H. Zhou. Learning from Incomplete and Inaccurate Supervision. In: IEEE Transactions on Knowledge and Data Engineering (TKDE), in press, 2021.

IEEE Transactions on Neural Networks and Learning Systems

  • J. Wang and Z.-H. Zhou. Margin Distribution Analysis. In: IEEE Transactions on Neural Networks and Learning Systems (TNNLS), in press, 2021.

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 (TPAMI), 2021, 43(11): 3878-3891.

  • Y.-F. Li, L.-Z. Guo, and Z.-H. Zhou. Towards safe weakly supervised learning. In: IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021, 43(1): 334-346.

IEEE Transactions on Visualization and Computer Graphics

  • C.-J. Chen, Z.-W. Wang, J. Wu, X.-T. Wang, L.-Z. Guo, Y.-F. Li, S.-X. Liu. Interactive Graph Construction for Graph-Based Semi-Supervised Learning. In: IEEE Transactions on Visualization and Computer Graphics (TVCG).

International Journal of Computer Vision

  • H.-J. Ye, H.-X. Hu, D.-C. Zhan. Learning Classifier Synthesis for Generalized Few-Shot Learning. In: International Journal of Computer Vision. 2021, Volume 129, Issue 6, Page: 1930 - 1953.

Journal of Machine Learning Research

  • P. Zhao, G.-H. Wang, L.-J. Zhang, and Z.-H. Zhou. Bandit Convex Optimization in Non-stationary Environments. In: Journal of Machine Learning Research, 2021, 22(125):1−45.

  • S.-Q. Zhang, Z.-Y. Zhang, and Z.-H. Zhou. Bifurcation Spiking Neural Network. In: Journal of Machine Learning Research, 2021, 22(253):1-21.

Machine Learning

  • W.-J. Shang, Q.-Y. Li, Z.-W. Qin, Y. Yu, Y.-P. Meng, and J.-P. Ye. Partially observable environment estimation with uplift inference for reinforcement learning based recommendation. In: Machine Learning, 2021, 110(9): 2603-2640.

Neural Computation

  • S.-Q. Zhang and Z.-H. Zhou. Flexible Transmitter Network. In: Neural Computation, in press, 2021.

Pattern Recognition

  • Y. Zhu, K.-M. Ting, M. Carman, M. Angelova. CDF Transform-and-Shift: An effective way to deal with datasets of inhomogeneous cluster densities. In: Pattern Recognition, 2021.

  • Y.-H. Cao, J.-X. Wu, H.-C. Wang, J. Lasenby. Neural Random Subspace. In: Pattern Recognition, 2021.

Science China Information Sciences

  • C. Bian, C. Qian, Y. Yu, and K. Tang. On the Robustness of Median Sampling in Noisy Evolutionary Optimization. In: Science China: Information Sciences, 2021, 64(5): 1-13.

  • S.-Y. Zhao, Y.-P. Xie, and W.-J. Li. On the Convergence and Improvement of Stochastic Normalized Gradient Descent. I In: Science China: Information Sciences, 2021

  • M. Xu, and L.-Z. Guo. Learning From Group Supervision: The Impact of Supervision Deficiency on Multi-Label Learning. In: Science China: Information Sciences, 2021

  • X.-C. Li, D.-C. Zhan, J.-Q. Yang, and Y. Shi. Deep multiple instance selection. In: Science China: Information Sciences, 2021

  • Z.-H. Zhou. Why over-parameterization of deep neural networks does not overfit? In: Science China: Information Sciences, 2021, 64(1): 116101.

软件学报

  • 陈子璇, 章宗长, 潘致远, 张琳婧. 一种基于广义异步值迭代的规划网络模型. In: 软件学报, 2021, 32(11): 3496-3511.

中国科学:信息科学

  • 赵鹏, 周志华. 基于决策树模型重用的分布变化流数据学习. In: 中国科学:信息科学, 2021, 51(1): 1-12.

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