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• J.-Q. Yang, K.-B. Fan, H. Ma and D.-C. Zhan. RID-Noise: Towards Robust Inverse Design under Noisy Environments. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22), 2022.

• L. Yuan, J. Wang, F. Zhang, C. Wang, Z. Zhang, Y. Yu and C. Zhang. Multi-Agent Incentive Communication via Decentralized Teammate Modeling. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22), 2022.

• Y.-X. Sun and W. Wang. Exploiting mixed unlabeled data for detecting samples of seen and unseen out-of-distribution classes. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22), 2022.

• Y.-H. Cao and J. Wu. A Random CNN Sees Objects: One Inductive Bias of CNN and Its Applications. In: Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI'22), 2022.

• Z.-M. Zhu, S. Jiang, Y.-R. Liu, Y. Yu and K. Zhang. Invariant action effect model for reinforcement learning. In: AAAI Conference on Artificial Intelligence (AAAI'22), 2022.

• Z.-X. Chen, X.-Q. Cai, Y. Jiang and Z.-H. Zhou. Anomaly Guided Policy Learning from Imperfect Demonstrations. In: Proceedings of the 21th International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'22), 2022.

• M.-J. Yuan and W. Gao. Learning with Interactive Models over Decision-Dependent Distributions. In: Proceedings of the 14th Asian Conference on Machine Learning (ACML'22), 2022.

• G. Wang, M. Yang, L. Zhang and T. Yang. Momentum Accelerates the Convergence of Stochastic AUPRC Maximization. In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS'22), 2022.

• P. Zhao, Y.-X. Wang and Z.-H. Zhou. Non-stationary Online Learning with Memory and Non-stochastic Control. In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS'22), 2022.

• Y. Tao, Y. Wu, P. Zhao and D. Wang. Optimal Rates of (Locally) Differentially Private Heavy-tailed Multi-Armed Bandits. In: Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS'22), 2022.

• S. Lu, Y. Miao, P. Yang, Y. Hu and L. Zhang. Non-Stationary Dueling Bandits for Online Learning to Rank. In: The Asia Pacific Web and Web-Age Information Management Joint International Conference on Web and Big Data (APWeb-WAIM'22), 2022.

• H. Luo, M. Zhang, P. Zhao and Z.-H. Zhou. Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits. In: Proceedings of the 35nd Annual Conference on Learning Theory (COLT'22), 2022.

• H. Luo, M. Zhang and P. Zhao. Adaptive Bandit Convex Optimization with Heterogeneous Curvature. In: Proceedings of the 35nd Annual Conference on Learning Theory (COLT'22), 2022.

• L. Sui, C.-L. Zhang and J. Wu. Salvage of Supervision in Weakly Supervised Object Detection. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'22), 2022.

• X.-C. Li, Y.-C. Xu, S. Song, B. Li, Y. Li, Y. Shao and D.-C. Zhan. Federated Learning with Position-Aware Neurons. In: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'22), 2022.

• M. Fu, Y.-H. Cao and J. Wu. Worst Case Matters for Few-Shot Recognition. In: European Conference on Computer Vision (ECCV'22), 2022.

• Y.-H. Cao, H. Yu and J. Wu. Training Vision Transformers with Only 2040 Images. In: European Conference on Computer Vision (ECCV'22), 2022.

• Y.-H. Cao, Y. Huang, P. Sun, J. Wu and S. Zhou. Synergistic Self-Supervised and Quantization Learning. In: European Conference on Computer Vision (ECCV'22), 2022.

• X.-C. Li, Y.-J. Wang, L. Gan and D.-C. Zhan. Exploring Transferability Measures and Domain Selection in Cross-Domain Slot Filling. In: IEEE International Conference on Acoustics, Speech and SP (ICASSP'22), 2022.

• Y.-F. Ma and M. Li. Learning from the Multi-Level Abstraction of the Control Flow Graph via Alternating Propagation for Bug Localization. In: Proceedings of the 22th IEEE International Conference on Data Mining (ICDM'22), 2022.

• H. Zhao, Y. Yu and K. X. Learning efficient online 3D bin packing on packing configuration trees. In: International Conference on Learning Representations (ICLR'22), 2022.

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• L. Zhang, G. Wang, J. Yi and T. Yang. A Simple yet Universal Strategy for Online Convex Optimization. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), 2022.

• L.-Z. Guo and Y.-F. Li. Class-Imbalanced Semi-Supervised Learning with Adaptive Thresholding. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), 2022.

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• P. Zhao, L.-F. Li and Z.-H. Zhou. Dynamic Regret of Online Markov Decision Processes. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), 2022.

• W. Jiang, B. Wang, Y. Wang, L. Zhang and T. Yang. Optimal Algorithms for Stochastic Multi-Level Compositional Optimization. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), 2022.

• Z. Yuan, Y. Wu, Z.-H. Qiu, X. Du, L. Zhang, D. Zhou and T. Yang. Provable Stochastic Optimization for Global Contrastive Learning: Small Batch Does Not Harm Performance. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), 2022.

• Z.-H. Qiu, Q. Hu, Y. Zhong, L. Zhang and T. Yang. Large-scale Stochastic Optimization of NDCG Surrogates for Deep Learning with Provable Convergence. In: Proceedings of the 39th International Conference on Machine Learning (ICML'22), 2022.

• C. Bian, Y. Zhou and C. Qian. Robust Subset Selection by Greedy and Evolutionary Pareto Optimization. In: Proceedings of the 31th International Joint Conference on Artificial Intelligence (IJCAI'22), 2022.

• C. Qian. Towards Theoretically Grounded Evolutionary Learning. In: Proceedings of the 31th International Joint Conference on Artificial Intelligence (IJCAI'22), 2022.

• D. Xue, L. Yuan, Z. Zhang and Y. Yu. Efficient Multi-Agent Communication via Shapley Message Value. In: Proceedings of the 31th International Joint Conference on Artificial Intelligence (IJCAI'22), 2022.

• H. Shang, J.-L. Wu, W. Hong and C. Qian. Neural Network Pruning by Cooperative Coevolution. In: Proceedings of the 31th International Joint Conference on Artificial Intelligence (IJCAI'22), 2022.

• L. Yuan, C. Wang, J. Wang, F. Zhang, F. Chen, C. Guan, Z. Zhang, C. Zhang and Y. Yu. Multi-Agent Concentrative Coordination with Decentralized Task Representation. In: Proceedings of the 31th International Joint Conference on Artificial Intelligence (IJCAI'22), 2022.

• M.-Z. Qian, Z. Ai, T. Zhang and W. Gao. On the Optimization of Margin Distribution. In: Proceedings of the 31th International Joint Conference on Artificial Intelligence (IJCAI'22), 2022.

• Y. Zhu and K.-M. Ting. Improving the Effectiveness and Efficiency of Stochastic Neighbour Embedding with Isolation Kernel (Extended Abstract). In: Proceedings of the 31th International Joint Conference on Artificial Intelligence (IJCAI'22), 2022.

• X.-C. Li, J.-L. Tang, S. Song, B. Li, Y. Li, Y. Shao, L. Gan and D.-C. Zhan. Avoid Overfitting User Specific Information in Federated Keyword Spotting. In: Proceedings of the 23rd Conference of the International Speech Communication Association (INTERSPEECH'22), 2022.

• Y.-K. Zhang, D.-W. Zhou, H.-J. Ye and D.-C. Zhan. Audio-Visual Generalized Few-Shot Learning with Prototype-Based Co-Adaptation. In: Proceedings of the 23rd Conference of the International Speech Communication Association (INTERSPEECH'22), 2022.

• J.-J. Shao, Y. Xu, Z. Cheng and Y.-F. Li. Active Model Adaptation Under Unknown Shift. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), 2022.

• X. Han, Y. Zhu, K.-M. Ting, D.-C. Zhan and G. Li. Streaming Hierarchical Clustering Based on Point-Set Kernel. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), 2022.

• Z.-Y. Zhang, Y.-Y. Qian, Y.-J. Zhang, Y. Jiang and Z.-H. Zhou. Adaptive Learning for Weakly Labeled Streams. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'22), 2022.

• C. Wu, T. Li, Z. Zhang and Y. Yu. Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• C. Guan, F. Chen, L. Yuan, C. Wang, H. Yin, Z. Zhang and Y. Yu. Efficient Multi-agent Communication via Self-supervised Information Aggregation. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• J.-J. Shao, L.-Z. Guo, X.-W. Yang and Y.-F. Li. Active Model Adaptation Under Changed Distributions. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• J.-Q. Yang and D.-C. Zhan. Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• K. Xue, J. Xu, L. Yuan, M. Li, C. Qian, Z. Zhang and Y. Yu. Multi-agent Dynamic Algorithm Configuration. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• L. Chai and M. Li. Pyramid Attention for Source Code Summarization. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• L. Zhang, W. Jiang, J. Yi and T. Yang. Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• L.-Z. Guo, Y.-G. Zhang, Z.-F. Wu, J.-J. Shao and Y.-F. Li. SU-SSL: Maximize Performance in Unseen Classes and Maintain Safeness in Seen Classes. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• L. Song, K. Xue, X. Huang and C. Qian. Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• P. Zhao, Y.-F. Xie, L. Zhang and Z.-H. Zhou. Efficient Methods for Non-stationary Online Learning. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• R. Qin, F. Chen, T. Wang, L. Yuan, X. Wu, Y. Kang, Z. Zhang, C. Zhang and Y. Yu. Multi-agent policy transfer via task relationship modeling. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22 Workshop on Deep RL), 2022.

• R.-J. Qin, S. Gao, X. Zhang, Z. Xu, S. Huang, Z. Li, W. Zhang and Y. Yu. NeoRL: A Near Real-World Benchmark for Offline Reinforcement Learning. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• S.-Q. Zhang and Z.-H. Zhou. Theoretically Provable Spiking Neural Networks. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• S.-H. Lyu, Y.-X. He and Z.-H. Zhou. Depth is More Powerful than Width with Prediction Concatenation in Deep Forest. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• S. Ding and W. Wang. Collaborative learning by detecting collaboration partners. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• T.-Z. Wang, T. Qin and Z.-H. Zhou. Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• W. Jiang, G. Li, Y. Wang, L. Zhang and T. Yang. Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• X.-C. Li, W.-S. Fan, S. Song, Y. Li, B. Li, Y. Shao and D.-C. Zhan. Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• Y. Wan, W.-W. Tu and L. Zhang. Online Frank-Wolfe with Unknown Delays. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• Y.-X. Ding, X.-Z. Wu, K. Zhou and Z.-H. Zhou. Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• Y. Bai, Y.-J. Zhang, P. Zhao, M. Sugiyama and Z.-H. Zhou. Adapting to Online Label Shift with Provable Guarantees. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• Z.-H. Tan, Y. Xie, Y. Jiang and Z.-H. Zhou. Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks. In: Advances in Neural Information Processing Systems 35 (NeurIPS'22), 2022.

• J. Li, J.-Q. Guo and W. Gao. Data Removal from an AUC Optimization Model. In: Proceedings of the26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'22), 2022.

• T. Wei, J.-X. Shi, Y.-F. Li and M.-L. Zhang. Prototypical Classifier for Robust Class-Imbalanced Learning. In: Proceedings of the26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'22), 2022.

• Y. Jian, J. Yi and L. Zhang. Adaptive Feature Generation for Online Continual Learning from Imbalanced Data. In: Proceedings of the26th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'22), 2022.

• C. Bian and C. Qian. Better Running Time of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) by Using Stochastic Tournament Selection. In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), 2022.

• J.-L. Wu, H. Shang, W. Hong and C. Qian. Robust Neural Network Pruning by Cooperative Coevolution. In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), 2022.

• Y.-C. Wu, Y.-X. He, C. Qian and Z.-H. Zhou. Multi-objective Evolutionary Ensemble Pruning Guided by Margin Distribution. In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), 2022.

• Z.-A. Zhang, C. Bian and C. Qian. Running Time Analysis of the (1+1)-EA using Surrogate Models on OneMax and LeadingOnes. In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), 2022.

• D.-M. Liu, H. Shang, W. Hong and C. Qian. Multi-Objective Evolutionary Instance Selection for Multi-Label Classification. In: Proceedings of the 19th Pacific Rim International Conference on Artificial Intelligence (PRICAI'22), 2022.

• S. Lu, Y.-H. Zhou, J.-C. Shi, W. Zhu, Q. Yu, Q.-G. Chen, Q. Da and L. Zhang. Non-stationary Continuum-armed Bandits for Online Hyperparameter Optimization. In: Proceedings of the15th ACM International Conference on Web Search and Data Mining (WSDM'22), 2022.

• C. Qian, D.-X. Liu and Z.-H. Zhou. Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees. In: Artificial Intelligence, 2022, 309: 103737.

• W. Gao, F. Xu and Z.-H. Zhou. Towards convergence rate analysis of random forests for classification. In: Artificial Intelligence, 2022, 313:103788.

• Z.-H. Zhou. Rehearsal: Learning from Prediction to Decision. In: Frontiers of Computer Science, 2022, 16(4): 164352.

• R.-Z. Liu, H. Guo, X. Ji, Y. Yu, Z.-J. Pang, Z. Xiao, Y. Wu and T. Lu. Efficient reinforcement learning for StarCraft by abstract forward models and transfer learning. In: IEEE Transactions on Games, 2022, 14(2):94-307.

• X. Song, S. Aryal, K.-M. Ting, Z. Liu and B. He. Spectral-Spatial Anomaly Detection of Hyperspectral Data Based on Improved Isolation Forest. In: IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 1-16 (2022).

• M. Pang, K.-M. Ting, P. Zhao and Z.-H. Zhou. Improving Deep Forest by Screening. In: IEEE Transactions on Knowledge and Data Engineering, 2022, 34(9): 4298-4312.

• 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, 2022, 34(12):5854-5868.

• G. Li, P. Yang, C. Qian, R. Hong and K. Tang. Stage-wise Magnitude-based Pruning for Recurrent Neural Networks. In: IEEE Transactions on Neural Networks and Learning Systems, 2022, in press.

• J. Wang and Z.-H. Zhou. Margin Distribution Analysis. In: IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(8): 3948-3960.

• J.-H. Wu, S.-Q. Zhang, Y. Jiang and Z.-H. Zhou. Theoretical Exploration of Flexible Transmitter Model. In: IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2022, in press.

• B.-J. Hou, L. Zhang and Z.-H. Zhou. Prediction With Unpredictable Feature Evolution. In: IEEE Transactions on Neural Networks and Learning Systems, 2022, 33(10): 5706-5715.

• H.-J. Ye, L. Han and D.-C. Zhan. Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, in press.

• T. Xu, Z. Li and Y. Yu. Error bounds of imitating policies and environments for reinforcement learning. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(10):6968-6980.

• Y.-Q. Hu, X.-H. Liu, S.-Q. Li and Y. Yu. Cascaded algorithm selection with extreme-region UCB bandit. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(10):6782-6794.

• Y. Wan and L. Zhang. Efficient Adaptive Online Learning via Frequent Directions. In: IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022, 44(10): 6910-6923.

• Y. Zhu, K.-M. Ting, Y. Jin and M. Angelova. Hierarchical clustering that takes advantage of both density-peak and density-connectivity. In: Information Systems, 2022, 103:101871(2022).

• R.-Z. Liu, Z.-J. Pang, Z.-Y. Meng, W. Wang, Y. Yu and T. Lu. On efficient reinforcement learning for full-length game of StarCraft II. In: Journal of Artificial Intelligence Research, 2022, in press.

• Y. Wan, G. Wang, W.-W. Tu and L. Zhang. Projection-free Distributed Online Learning with Sublinear Communication Complexity. In: Journal of Machine Learning Research, 2022, 23(172): 1-53.

• P. Tan, Z.-H. Tan, Y. Jiang and Z.-H. Zhou. Towards Enabling Learnware to Handle Heterogeneous Feature Spaces. In: Machine Learning, 2022, in press.

• Y. Wan, W.-W. Tu and L. Zhang. Online Strongly Convex Optimization with Unknown Delays. In: Machine Learning, 2022, 111(3): 871-893.

• Y.-F. Ma and M. Li. The Flowing Nature Matters: Feature Learning from the Control Flow Graph of Source Code for Bug Localization. In: Machine Learning, 2022, 111(3): 853-870.

• Y.-F. Zhang, F.-M. Luo and Y. Yu. Improve generated adversarial imitation learning with reward variance regularization. In: Machine Learning, 2022, 111:977-995.

• Z.-H. Zhou. Open-environment machine learning. In: National Science Review, 2022, 9(8): nwac123.

• S.-Q. Zhang, W. Gao and Z.-H. Zhou. Towards Understanding Theoretical Advantages of Complex-Reaction Networks. In: Neural Networks, 2022, 151:80-93.

• S.-H. Lyu, L. Wang and Z.-H. Zhou. Improving Generalization of Neural Networks by Leveraging Margin Distribution. In: Neural Networks, 2022, 151:48-60.

• K.-M. Ting, Z. Liu, H. Zhang and Y. Zhu. A New Distributional Treatment for Time Series and An Anomaly Detection Investigation. In: Proceedings of the VLDB Endowment, 2022, 15(11): 2321-2333.

• H. Sun and M. Li. Enhancing Unsupervised Domain Adaptation by Exploiting the Conceptual Consistency of Multiple Self-Supervised Tasks. In: Science China: Information Sciences, 2022, in press.

• J. Cao, L. Yuan, J. Wang, S. Zhang, C. Zhang, Y. Yu and D.-C. Zhan. LINDA: Multi-Agent Local Information Decomposition for Awareness of Teammates. In: Science China: Information Sciences, 2022, in press.

• T. Wei, H. Wang, W.-W. Tu and Y.-F. Li. Robust model selection for positive and unlabeled learning with constraints. In: Science China: Information Sciences, 2022, 65(11): 212101.

• Y. Wan, W.-W. Tu and L. Zhang. Strongly Adaptive Online Learning over Partial Intervals. In: Science China: Information Sciences, 2022, 65(10): 202101.

• Y.-R. Liu, Y.-Q. Hu, H. Qian, Y. Yu and C. Qian. ZOOpt: Toolbox for Derivative-Free Optimization. In: Science China: Information Sciences, 2022, 65: 20710.

• L. Han, H.-J. Ye and D.-C. Zhan. On Pseudo-Labeling for Class-Mismatch Semi-Supervised Learning. In: Transactions on Machine Learning Research, 2022, in press.

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2021¶

[Conference Paper][Journal Article]

Conference Paper

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

• 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.

Top

Journal Article and Book

• 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.

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

• 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.

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

• 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.

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

• 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.

• 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.

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

• 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.

• 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).

• 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.

• 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.

• 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.

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

• 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.

• 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.

• 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.

• 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.

Top

2020¶

[Conference Paper][Journal Article]

Conference Paper

• 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.

• Y.-X. Ding and Z.-H. Zhou. Boosting-based reliable model reuse. In: Proceedings of the 12th Asian Conference on Machine Learning (ACML'20), 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.

• 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.

• 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.

• 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.

• Y.-X. Huang, W.-Z. Dai, J. Yang, L.-W. Cai, S.-F. Cheng, R.-Z. Huang, Y.-F. Li, and Z.-H. Zhou. Semi-supervised abductive learning and its application to theft judicial sentencing. In: Proceedings of the 20th IEEE International Conference on Data Mining (ICDM'20), 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.-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.

• 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.

• 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), 2020.

• 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.

• 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.

• L. Wang, X.-Q. Liu, J.-F. Yi, Y. Jiang, Cho-Jui Hsieh. Provably Robust Metric Learning. In: Advances in Neural Information Processing Systems 33 (NeurIPS’20), 2020.

• P. Zhao, Y.-J. Zhang, L. Zhang, Z.-H. Zhou. Dynamic regret of convex and smooth functions. In: Advances in Neural Information Processing Systems 33 (NeurIPS’20), 2020.

• S.-Y. Jiang, J.-C. Pang, Y. Yu. Offline Imitation Learning with a Misspecified Simulator. In: Advances in Neural Information Processing Systems 33 (NeurIPS’20), 2020.

• T. Xu, Z.-N. Li, Y. Yu. Error Bounds of Imitating Policies and Environments. In: Advances in Neural Information Processing Systems 33 (NeurIPS’20), 2020.

• W. Gao, Z.-H. Zhou. Towards convergence rate analysis of random forests for classification. In: Advances in Neural Information Processing Systems 33 (NeurIPS’20), 2020.

• Y.-J. Zhang, P. Zhao, L. Ma, Z.-H. Zhou. An unbiased risk estimator for learning with augmented classes. In: Advances in Neural Information Processing Systems 33 (NeurIPS’20), 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.

• 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.

• 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.

Top

Journal Article and Book

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

• 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.

• 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.

• B.-J. Hou and Z.-H. Zhou. Learning with interpretable structure from gated RNN. In: IEEE Transactions on Neural Networks and Learning Systems, 2020.

• T. Wei, Y.-F. Li. Does Tail Label Help for Large-Scale Multi-Label Learning. In: IEEE Transactions on Neural Network and Learning Systems, 2020.

• 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.

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

• L. Wang, H. Zhang, J.-F. Yi, C.-J. Hsieh and Y. Jiang. Spanning attack: reinforce black-box attacks with unlabeled data. In: Machine Learning, in press, 2020.

• 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.

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

• L. Wang and J. Yuan. Robustness verification of K-NN classifiers via constraint relaxation and randomized smoothing. In: Science China Information Sciences, in press.

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

• 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.

• 贺一笑, 庞明, 姜远. 蒙德里安深度森林. In: 计算机研究与发展, 2020.

• 吴建鑫 著, 罗建豪, 张皓 译. 《模式识别》 北京: 机械工业出版社, 2020. (ISBN 978-7-111-64389-0)

• 周志华, 王魏, 高尉, 张利军 著. 《机器学习理论导引》 北京: 机械工业出版社, 2020. (ISBN 978-7-111-65424-7)

• 周志华 著, 李楠 译. 《集成学习: 基础与算法》 北京: 电子工业出版社, 2020. (ISBN 978-7-121-39077-7)

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2019¶

[Conference Paper][Journal Article]

Conference Paper

• W.-L. Chao, H.-J. Ye, D.-C. Zhan, M. Campbell, K.-Q. Weinberger. A Meta Understanding of Meta-Learning. In: The Adaptive and Multitask Learning (AMTL) 2019 Workshop, Long Beach, CA, 2019.

• H.-J. Ye, X.-R. Sheng, D.-C. Zhan. Few-Shot Learning with Adaptively Initialized Task Optimizer. In: Proceedings of the 11th Asian Conference on Machine Learning (ACML'19), Nagoya, Japan, 2019.

• 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

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

• C. Qian, C. Bian, Y. Yu, K. Tang, and X. Yao. Analysis of noisy evolutionary optimization when sampling fails. In: Algorithmica, in press.

• 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, in press.

• C. Qian. Distributed Pareto Optimization for Large-scale Noisy Subset Selection. In: IEEE Transactions on Evolutionary Computation, in press.

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

Top

2018¶

[Conference Paper][Journal Article]

Conference Paper

• L. Zhang, and Z.-H. Zhou. \ell_1-regression with Heavy-tailed Distributions. In: Advances in Neural Information Processing Systems 31 (NIPS 2018), to appear, 2018.

• L. Zhang, S. Lu, and Z.-H. Zhou. Adaptive Online Learning in Dynamic Environments. In: Advances in Neural Information Processing Systems 31 (NIPS 2018), to appear, 2018.

• M. Liu, X. Zhang, L. Zhang, R. Jin, and T. Yang. Fast Rates of ERM and Stochastic Approximation: Adaptive to Error Bound Conditions. In: Advances in Neural Information Processing Systems 31 (NIPS 2018), to appear, 2018.

• J. Feng, Y. Yu, Z.-H. Zhou. Multi-layered gradient boosting decision trees. In: Advances in Neural Information Processing Systems 31 (NIPS'18), Montreal, Canada, 2018.

• S.-Y. Zhao, G.-D. Zhang, M.-W. Li, W.-J. Li. Proximal SCOPE for Distributed Sparse Learning. In: Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS), 2018.

• Y.-X. Ding and Z.-H. Zhou. Preference Based Adaptation for Learning Objectives. In: Proceedings of the Annual Conference on Neural Information Processing Systems (NIPS), 2018.

• Y. Yu. Towards sample efficient reinforcement learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) (Early Career), Stockholm, Sweden, 2018.

• X. Huo, Y. Yang, M. Li, D.-C. Zhan. Learning Semantic Features for Software Defect Prediction by Code Comments Embedding. In: Proceedings of the 2018 IEEE International Conference on Data Mining (ICDM'2018), Singapore, 2018.

• Y.-F. Wu, D.-C. Zhan, Y. Jiang. DMTMV: A Unified Learning Framework for Deep Multi-Task Multi-View Learning. In: Proceedings of the 2018 IEEE International Conference on Big Knowledge (ICBK'2018), Singapore, 2018.

• P. Li, J. Yi, and L. Zhang. Query-Efficient Black-Box Attack by Active Learning. In: Proceedings of the 18th IEEE International Conference on Data Mining (ICDM 2018), to appear, 2018.

• Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales, W.-W. Tu, Y. Yu, Lisheng Sun-Hosoya, Isabelle Guyon, and Michele Sebag. Towards AutoML in the presence of drift: First results. In: ICML 2018 Workshop on AutoML, Stockholm, Sweden, 2018.

• L. Zhang, T. Yang, R. Jin, and Z.-H. Zhou. Dynamic regret of strongly adaptive methods. In: Proceedings of the 35th International Conference on Machine Learning (ICML'18), Stockholm, Sweden, 2018.

• H.-J. Ye, D.-C. Zhan, Y. Jiang, Z.-H. Zhou. Rectify Heterogeneous Model with Semantic Mapping. In: Proceedings of the 35th International Conference on Machine Learning (ICML'18), Stockholm, Sweden, 2018.

• K. M. Ting, Y. Zhu, and Z.-H. Zhou. Isolation kernel and its effect to SVM. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18), London, UK, 2018.

• Y. Yang, Y.-F. Wu, D.-C. Zhan, Z.-B. Liu, Y. Jiang. Complex Object Classification: A Multi-Modal Multi-Instance Multi-Label Deep Network with Optimal Transport. In: Proceedings of the Annual Conference on ACM SIGKDD (KDD'18), London, UK, 2018.

• S.-Y. Chen, Y. Yu, Q. Da, J. Tan, H.-K. Huang and H.-H. Tang. Stablizing reinforcement learning in dynamic environment with application to online recommendation. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18) (Research Track), London, UK, 2018.

• Y.-J. Hu, Q. Da, A.-X. Zeng, Y. Yu and Y.-H. Xu. Reinforcement learning to rank in e-commerce search engine: Formalization, analysis, and application. In: Proceedings of the 24th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'18) (Applied Track), London, UK, 2018.

• C. Qian, C. Bian, Y. Yu, K. Tang, and X. Yao. Analysis of noisy evolutionary optimization when sampling fails. In: Proceedings of the 20th ACM Conference on Genetic and Evolutionary Computation (GECCO'18), Kyoto, Japan, 2018.

• T. Zhang and Z.-H. Zhou. Semi-supervised optimal margin distribution machines. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

• D.-D. Chen, W. Wang, W. Gao, and Z.-H. Zhou. Tri-net for semi-supervised deep learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

• C. Zhang, Y. Yu, and Z.-H. Zhou. Learning environmental calibration actions for policy self-evolution. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

• Y.-Q. Hu, Y. Yu, and Z.-H. Zhou. Experienced optimization with reusable directional model for hyper-parameter search. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

• H.-H. Wei and M. Li. Positive and unlabeled learning for detecting software functional clones with adversarial training. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

• Z. Xie and M. Li. Cutting the Software Building Efforts in Continuous Integration by Semi-Supervised Online AUC Optimization. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) , Stockholm, Sweden, 2018.

• H.-J. Ye, X.-R. Sheng, D.-C. Zhan, P. He. Distance Metric Facilitated Transportation between Heterogeneous Domains. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• Y. Yang, D.-C. Zhan, X.-R. Sheng, Y. Jiang. Semi-Supervised Multi-Modal Learning with Incomplete Modalities. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• Y. Yu, W.-J. Zhou. Mixture of GANs for clustering. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• G.-H. Wang, D. Zhao, and L.-J. Zhang. Minimizing Adaptive Regret with One Gradient per Iteration.'In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• Y.-Y. Wan, N. Wei, and L.-J. Zhang. Efficient Adaptive Online Learning via Frequent Directions.In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• B.-B. Gao, H.-Y. Zhou, J.-X. Wu, X. Geng. Age Estimation Using Expectation of Label Distribution Learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• C. Qian, Y. Yu, K. Tang. Approximation guarantees of stochastic greedy algorithms for subset selection. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• T. Wei, Y.-F. Li. Does tail label help for large-scale multi-label learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• D.-M. Liang, Y.-F. Li. Lightweight label propagation for large-scale network data. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018.

• J. Feng and Z.-H. Zhou. AutoEncoder by forest. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• T. Zhang and Z.-H. Zhou. Optimal margin distribution clustering. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• P. Zhao and Z.-H. Zhou. Label distribution learning by optimal transport. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• H.-C. Dong, Y.-F. Li, and Z.-H. Zhou. Learning from semi-supervised weak-label data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• C. Liu, P. Zhao, S.-J. Huang, Y. Jiang, and Z.-H. Zhou. Dual set multi-label learning. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• H. Wang, H. Qian, and Y. Yu. Noisy derivative-free optimization with value suppression. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI’18) , New Orleans, LA, 2018.

• Z. Xie and M. Li. Semi-supervised AUC optimization without guessing labels of unlabeled data. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• Q.-Y. Jiang and W.-J. Li. Asymmetric Deep Supervised Hashing. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• L.-Z. Guo, Y.-F. Li. A general formulation for safely exploiting weakly supervised data. In: Proceedings of the 32nd AAAI conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• W.-Y. Lin, Y. Mi, J.-X. Wu, K.Lu and H.-K. Xiong. Action Recognition with Coarse-to-Fine Deep Feature Integration and Asynchronous Fusion. In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18) , New Orleans, LA, 2018.

• Y. Yang, Y.-F. Wu, D.-C. Zhan, Y. Jiang. Multi-Network User Identification via Graph-Aware Embedding. In: Proceedings of the 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18) , Melbourne, Australia, 2018.

Top

Journal Article and Book

• H.-J. Ye, D.-C. Zhan, Y. Jiang. Fast Generalization Rates for Distance Metric Learning. Machine Learning, in press.

• E. Sansone, F. G. B. De Natale, and Z.-H. Zhou. Efficient training for positive unlabeled learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.

• S.-J. Huang, W. Gao, and Z.-H. Zhou. Fast multi-instance multi-label learning. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.

• S.-Y. Li, Y. Jiang, N. V. Chawla, and Z.-H. Zhou. Multi-label learning from crowds. IEEE Transactions on Knowledge and Data Engineering, in press.

• K. M. Ting, Y. Zhu, M. Carman, Y. Zhu, T. Washio, and Z.-H. Zhou. Lowest probability mass neighbor algorithms: Relaxing the metric constraint in distance-based neighbourhood algorithms. Machine Learning, in press.

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

• 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.

• J.-X. Wu, B.-B. Gao, X.-S. Wei, J.-H. Luo. 资源受限的深度学习：挑战与实践(in Chinese). 中国科学: 信息科学(SCIENTIA SINICA Informationis), 48(5), 2018: 501-510.

• Q.-Y. Jiang, X. Cui, W.-J. Li. Deep Discrete Supervised Hashing. IEEE Transactions on Image Processing (TIP).

• H.-J. Ye, D.-C. Zhan, Y. Jiang, Z.-H. Zhou. What Makes Objects Similar: A Unified Multi-Metric Learning Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence. DOI:10.1109/TPAMI.2018. 2829192.

• X.-Y. Guo and W. Wang. Towards making co-training suffer less from insufficient views. Frontiers of Computer Science, in press.

• Y. Zhu, K. M. Ting, and Z.-H. Zhou. Multi-label learning with emerging new labels. IEEE Transactions on Knowledge and Data Engineering, in press.

• Y. Zhu, J. Kwok, and Z.-H. Zhou. Multi-label learning with global and local correlation. IEEE Transactions on Knowledge and Data Engineering, in press.

• C. Hou and Z.-H. Zhou. One-pass learning with incremental and decremental features. IEEE Transactions on Pattern Analysis and Machine Intelligence, in press.

• Y. Yu, S.-Y. Chen, Q. Da, and Z.-H. Zhou. Reusable reinforcement learning via shallow trails. IEEE Transactions on Neural Networks and Learning Systems, in press.

• Y.-X. Ding and Z.-H. Zhou. Crowdsourcing with unsure option. Machine Learning, in press.

• T. Wei, L.-Z. Guo, Y.-F. Li, We. Gao. Learning safe multi-label prediction for weakly labeled data. Machine Learning. 107(4): 703-725, 2018.

• H. Wang, S.-B. Wang, Y.-F. Li. Instance selection method for improving graph-based semi-supervised learning. Frontiers of Computer Science. In press.

• C. Qian, J.-C. Shi, K. Tang, and Z.-H. Zhou. Constrained monotone k-submodular function maximization using multi-objective evolutionary algorithms with theoretical guarantee. IEEE Transactions on Evolutionary Computation, in press.

• X.-S. Wei, C.-L. Zhang, H. Zhang, J.-X. Wu. Deep Bimodal Regression of Apparent Personality Traits from Short Video Sequences. IEEE Transactions on Affective Computing.

• X.-S Wei, C.-W Xie, J.-X Wu, and C.-H Shen. Mask-CNN: Localizing parts and selecting descriptors for fine-grained bird species categorization. Pattern Recognition , 76, 2018: 704-714.

• C. Qian, Y. Yu, and Z.-H. Zhou. Analyzing evolutionary optimization in noisy environments. Evolutionary Computation , 2018, in press.

• C. Qian, Y. Yu, K. Tang, Y.-C Jin, X. Yao, and Z.-H. Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. Evolutionary Computation , 2018, in press.

• T. Sun and Z.-H. Zhou. Structural diversity of decision tree ensemble learning. Frontiers of Computer Science, in press.

• Z.-H. Zhou. A brief introduction to weakly supervised learning. National Science Review, 2018, 5(1): 44-53.

Top

2017¶

[Conference Paper][Journal Article]

Conference Paper

• W.-Z. Dai and Z.-H. Zhou. Combining logic abduction and statistical induction: Discovering written primitives with human knowledge. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

• B.-J. Hou, L. Zhang, and Z.-H. Zhou. Learning with feature evolvable streams. In: Advances in Neural Information Processing Systems 30 (NIPS'17), Long Beach, CA, 2017.

• C. Qian, J.-C. Shi, Y. Yu, K. Tang, and Z.-H. Zhou. Subset selection under noise. In: Advances in Neural Information Processing Systems 30 (NIPS'17), Long Beach, CA, 2017.

• L. Zhang, T. Yang, J. Yi, R. Jin, and Z.-H. Zhou. Improved dynamic regret for non-degeneracy functions. In: Advances in Neural Information Processing Systems 30 (NIPS'17), Long Beach, CA, 2017.

• Jing-Cheng Shi, Chao Qian, and Yang Yu. Evolutionary Multi-objective Optimization Made Faster by Sequential Decomposition. In: Proceedings of the 2017 IEEE Congress on Evolutionary Computation (CEC'17), San Sebastian, Spain, 2017.

• Y. Zhu, K. M. Ting, and Z.-H. Zhou. New class adaptation via instance generation in one-pass class incremental learning. In: Proceedings of the 17th IEEE International Conference on Data Mining (ICDM'17), New Orleans, LA, 2017.

• D. Ding, M. Zhang, S.-Y. Li, J. Tang, X. Chen, and Z.-H. Zhou. BayDNN: Friend recommendation with Bayesian personalized ranking deep neural network. In: Proceedings of the 26th ACM International Conference on Information and Knowledge Management (CIKM'17), Singapore, 2017.

• W.-Z. Dai, S. H. Muggleton, J. Wen, A. Tamaddoni-Nezhad, and Z.-H. Zhou. Logic vision: One-shot meta-intepretive learning from real images. In: Proceedings of the 25th International Conference on Inductive Logic Programming (ILP'17), Orleans, France, 2017.

• L. Zhang, T. Yang, R. Jin. Empirical Risk Minimization for Stochastic Convex Optimization: O(1/n)- and O(1/n^2 )-type of Risk Bounds. In: Proceedings of the 2017 edition of the Conference On Learning Theory (COLT'17), Amsterdam, Netherlands.

• T. Yang, Q. Lin, L. Zhang. A Richer Theory of Convex Constrained Optimization with Reduced Projections and Improved Rates. In: Proceedings of the 34th International Conference on Machine Learning (ICML'17), Sydney, Australia, 2017.

• X.-Z. Wu and Z.-H. Zhou. A unified view of multi-label performance measures. In: Proceedings of the 34th International Conference on Machine Learning (ICML'17), Sydney, Australia, 2017.

• T. Zhang and Z.-H. Zhou. Multi-class optimal distribution machine. In: Proceedings of the 34th International Conference on Machine Learning (ICML'17), Sydney, Australia, 2017.

• H.-Y. Zhou and J.-X. Wu. Content-Based Image Recovery In: Proc. Pacific-Rim Conference on Multimedia (PCM 2017), Harbin, China, October 2017.

• C.-L. Zhang, J.-H. Luo, Xiu-Shen Wei, J.-X. Wu. In Defense of Fully Connected Layers in Visual Representation Transfer? In: Proc. Pacific-Rim Conference on Multimedia (PCM 2017), Harbin, China, October 2017.

• H.-Y. Zhou, Bin-Bin Gao, J.-X. Wu. Adaptive Feeding: Achieving Fast and Accurate Detections by Adaptively Combining Object Detectors In: Proc. International Conference on Computer Vision (ICCV 2017), Venice, Italy, October 2017.

• J.-H. Luo, J.-X. Wu, Weiyao Lin. ThiNet: A Filter Level Pruning Method for Deep Neural Network Compression In: Proc. International Conference on Computer Vision (ICCV 2017), Venice, Italy, October 2017.

• H.-Y. Zhou, Bin-Bin Gao, J.-X. Wu. Sunrise or Sunset: Selective Comparison Learning for Subtle Attribute Recognition.In: Proc. The 28th British Machine Vision Conference (BMVC 2017), London, UK, September 2017.

• Y. Yang, D.-C. Zhan, Y. Fan, Y. Jiang. Instance Specific Discriminative Modal Pursuit: A Serialized Approach. In: Proceedings of the 9th Asian Conference on Machine Learning (ACML'17), Seoul, Korea, 2017.

• H.-J. Ye, D.-C. Zhan, X.-M. Si, Y. Jiang. Learning Mahalanobis Distance Metric: Considering Instance Disturbance Helps. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• Y. Yang, D.-C. Zhan, X.-Y. Guo, Y. Jiang. Modal Consistency based Pre-trained Multi-Model Reuse. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• Y. Zhang, Y. Jiang. Multimodal Linear Discriminant Analysis via Structural Sparsity. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• X. Huo, M. Li. Enhancing the Unified Features to Locate Buggy Files by Exploiting the Sequential Nature of Source Code. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• H.-H. Wei, M. Li. Supervised Deep Features for Software Functional Clone Detection Exploiting Lexical and Syntactical Information in Source Code. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• Y. Yu, W.-Y. Qu, N. Li, Z. Guo. Open Category Classification by Adversarial Sample Generation. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• W.-J. Zhou, Y. Yu, M.-L. Zhang. Binary Linear Compression for Multi-label Classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• J.-W. Yang, Y. Yu, X.-P. Zhang. Life-Stage Modeling by Customer-Manifold Embedding. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• C. Qian, J.-C. Shi, Y. Yu, K. Tang. On Subset Selection with General Cost Constraints. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• J. Zhang, Y. Sun, S.-J. Huang, N. Cam-Tu, X. Wang, X.-Y. Dai, J. Chen, Y. Yu. AGRA: An Analysis-Generation-Ranking Framework for Automatic Abbreviation from Paper Titles. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• X. Yan, L. Zhang, W.-J. Li. Semi-Supervised Deep Hashing with a Bipartite Graph. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• Y. Xiao, Z. Li, T. Yang, L. Zhang. SVD-free Convex-Concave Approaches for Nuclear Norm Regularization. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• Z.-H. Zhou and J. Feng. Deep forest: Towards an alternative to deep neural networks. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• M. Xu and Z.-H. Zhou. Incomplete label distribution learning. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• Y.-L. Zhang and Z.-H. Zhou. Multi-instance learning with key instance shift. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• B.-J. Hou, L. Zhang, and Z.-H. Zhou. Storage fit learning with unlabeled data. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• S.-J. Huang, J.-L. Chen, X. Mu, and Z.-H. Zhou. Cost-effective active learning from diverse labelers. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• W. Wang, X.-Y. Guo, S.-Y. Li, Y. Jiang, and Z.-H. Zhou. Obtaining high-quality label by distinguishing between easy and hard items in crowdsourcing. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• C. Qian, J.-C. Shi, Y. Yu, K. Tang, and Z.-H. Zhou. Optimizing ratio of monotone set functions. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• X.-S. Wei, C.-L. Zhang, Y. Li, C.-W. Xie, J. Wu, C. Shen, and Z.-H. Zhou. Deep descriptor transforming for image co-localization. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017.

• A.-S. Ni and M. Li. Cost-effective build outcome prediction using cascaded classifiers. In: Proceedings of the 14th International Conference on Mining Software Repositories (MSR'17), Buenous Aires, Argentina, 2017.

• Q.-Y. Jiang and W.-J. Li. Deep Cross-Modal Hashing. In: Proceedings of the 30th IEEE Conference on Computer Vision and Pattern Recognition (CVPR'17), Honolulu, Hawaii, 2017.

• P. Zhao, Y. Jiang, and Z.-H. Zhou. Multi-view matrix completion for clustering with side information. In: Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'17), LNAI, Jeju, Korea, 2017.

• H. Qian and Y. Yu. Solving high-dimensional multi-objective optimization problems with low effective dimensions. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17), San Francisco, CA, 2017.

• Y.-Q. Hu, H. Qian, and Y. Yu. Sequential classification-based optimization for direct policy search. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI’17), San Francisco, CA, 2017.

• J. Zhang, and L. Zhang. Efficient Stochastic Optimization for Low-Rank Distance Metric Learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

• Y. Xu, H. Yang, L. Zhang, and T. Yang. Efficient Non-oblivious Randomized Reduction for Risk Minimization with Improved Excess Risk Guarantee. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

• Z. Li, T. Yang, L. Zhang, and R. Jin. A Two-stage Approach for Learning a Sparse Model with Sharp Excess Risk Analysis. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

• J. Feng and Z.-H. Zhou. DeepMIML network. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

• Y.-F. Li, H.-W. Zha, and Z.-H. Zhou. Construct safe prediction from multiple regressors. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

• Y. Zhu, K. M. Ting, and Z.-H. Zhou. Discover multiple novel labels in multi-instance multi-label learning. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

• Y. Yang, D.-C. Zhan, Y. Fan, Y. Jiang, and Z.-H. Zhou. Deep learning for fixed model reuse. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

• X. Mu, F. Zhu, J. Du, E.-P. Lim, and Z.-H. Zhou. Streaming classification with emerging new class by class matrix sketching. In: Proceedings of the 31st AAAI Conference on Artificial Intelligence (AAAI'17), San Francisco, CA, 2017.

Top

Journal Article and Book

• W. Zhang, L.-J. Zhang, Z. Jin, R. Jin, D. Cai, X. Li, R. Liang, and X. He. Sparse Learning with Stochastic Composite Optimization. IEEE Transactions on Pattern Analysis & Machine Intelligence (TPAMI), 39(6): 1223 - 1236, 2017.

• Z.-H. Zhou. A brief introduction to weakly supervised learning. National Science Review, in press.

• C. Qian, J.-C. Shi, K. Tang, and Z.-H. Zhou. Constrained monotone k-submodular function maximization using multi-objective evolutionary algorithms with theoretical guarantee. IEEE Transactions on Evolutionary Computation, in press.

• C. Qian, Y. Yu, K. Tang, Y. Jin, X. Yao, and Z.-H. Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. Evolutionary Computation, in press.

• C. Qian, Y. Yu, and Z.-H. Zhou. Analyzing evolutionary optimization in noisy environments. Evolutionary Computation, in press.

• X. Mu, K. M. Ting, and Z.-H. Zhou. Classification under streaming emerging new classes: A solution using completely-random trees. IEEE Transactions on Knowledge and Data Engineering, 2017, 29(8): 1605-1618.

• B.-B. Gao, C. Xing, C.-W. Xie, J. Wu, and X. Geng. Deep Label Distribution Learning with Label Ambiguity. IEEE Transactions on Image Processing, 26(6), 2017: 2825-2838.

• X.-S. Wei, J.-H. Luo, J.n Wu, and Z.-H. Zhou. Selective Convolutional Descriptor Aggregation for Fine-Grained Image Retrieval. IEEE Transactions on Image Processing, 26(6), 2017: 2868-2881.

• W. Lin, Y. Shen, J. Yan, M.g Xu, J. Wu, J. Wang, and K. Lu. Learning Correspondence Structures for Person Re-identification. IEEE Transactions on Image Processing, 26(5), 2017: 2438-2453.

• G. Lin, F. Liu, C. Shen, J. Wu, H.-T. Shen. Structured Learning of Binary Codes with Column Generation for Optimizing Ranking Measures. International Journal of Computer Vision, 123(2), 2017: 287-308.

• J.-H. Luo, W. Zhou, J. Wu. Image Categorization with Resource Constraints: Introduction, Challenges and Advances. Frontiers of Computer Science, 11(1), 2017: pp. 13-26.

• C. Qian, Y. Yu, K. Tang, Y. Jin, X. Yao, and Z.-H. Zhou. On the Effectiveness of Sampling for Evolutionary Optimization in Noisy Environments. Evolutionary Computation, 2017, in press.

• D.-C. Zhan, J. Tang, and Z.-H. Zhou. Online Game Props Recommendation with Real Assessments. Complex & Intelligent Systems. 2017, DOI: 10.1007/s40747-016-0031-7

• S. Yang, and L. Zhang. Non-redundant Multiple Clustering by Nonnegative Matrix Factorization. Machine Learning, 106(5): 695 - 712, 2017.

• X.-S. Wei, J. Wu, and Z.-H. Zhou. Scalable algorithms for multi-instance learning. IEEE Transactions on Neural Networks and Learning Systems, 2017, 28(4): 975-987.

Top

2016¶

[Conference Paper][Journal Article]

Conference Paper

• H.-J. Ye, D.-C. Zhan, X.-M. Si, Y. Jiang and Z.-H. Zhou. What makes objects similar: a unified multi-metric learning approach. In: Proceedings of 30th Advances in Neural Information Processing Systems 29 (NIPS'16). P1235-1243

• H.-J. Ye, D.-C. Zhan, X.-L. Li, Z.-C. Huang and Y. Jiang. College student scholarships and subsidies granting: a multi-modal multi-label approach. In: Proceedings of the 16th IEEE International Conference on Data Mining (ICDM'16).

• Y. Zhu, K. M. Ting and Z.-H. Zhou. Multi-label learning with emerging new labels. In: Proceedings of the 16th IEEE International Conference on Data Mining (ICDM'16).

• W. Gao, X.-Y. Niu and Z.-H. Zhou. Learnability of Non-I.I.D.. In: Proceedings of the 8th Asian Conference on Machine Learning (ACML'16). P158-173

• H.-J. Ye, D.-C. Zhan, X.-M. Si and Y. Jiang. Learning feature aware metric. In: Proceedings of the 8th Asian Conference on Machine Learning (ACML'16). P286-301

• L.-J. Zhang, T. Yang, R. Jin and Z.-H. Zhou. Sparse Learning for Large-scale and High-dimensional Data: A Randomized Convex-concave Optimization Approach. In: Proceedings of the 27th International Conference on Algorithmic Learning theory (ALT'16). P83-97

• C. Qian, Y. Yu and Z.-H. Zhou. A lower bound analysis of population-based evolutionary algorithms for pseudo-Boolean functions. In: Proceedings of the 17th International Conference on Intelligent Data Enginering and Automated Learning (IDEAL'16). P457-467

• C. Qian, K. Tang and Z.-H. Zhou. Selection hyper-heuristics can provably be helpful in evolutionary multi-objective optimization. In: Proceedings of the 14th International Conference on Parallel Problem Solving from Nature (PPSN'16). P835-846

• X. Mu, F. Zhu, E. Lim, J. Xiao, J. Wang and Z.-H. Zhou. User identity linkage by latent user space modelling. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'16). P1775-1784

• K. Ting, Y. Zhu, M. Carman, Y. Zhu and Z.-H. Zhou. Overcoming key weaknesses of distance-based neighbourhood methods using a data dependent dissimilarity measure. In: Proceedings of the 22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'16). P1205-1214

• H. Wang, S.-B. Wang and Y.-F. Li. Instance selection Method for Improving Graph-Based Semi-supervised Learning. In: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI'16). P565-573

• X.-D. Wang and Z.-H. Zhou. Facial age estimation by total order preserving projections. In: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI'16). P603-615

• H. Wang and Y. Yu. Exploring multi-action relationship in reinforcement learning. In: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI'16). P574–587

• H. Qian and Y. Yu. On sampling and classification optimization in discrete domains. In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC'16). P4374-4381

• J. Chen, T. Yang, Q. Lin, L.-J. Zhang and Y. Chang. Optimal stochastic strongly convex optimization with a logarithmic number of projections. In: Proceedings of the 32nd Conference on Uncertainty in Artificial Intelligence (UAI'16). P122-131

• H. Yang, T. Zhou, Y. Zhang, B. Gao, J. Wu and J. Cai. Exploit bounding box annotations for multi-label object recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR'16). P280-288

• L.-J. Zhang, T. Yang, R. Jin, Y. Xiao and Z.-H. Zhou. Online stochastic linear optimization under one-bit feedback. In: Proceedings of the 33rd International Conference on Machine Learning (ICML'16). P392- 401

• T. Yang, L.-J. Zhang, R. Jin and J. Yi. Tracking slowly moving clairvoyant: optimal dynamic regret of online learning with true and noisy gradient. In: Proceedings of the 33rd International Conference on Machine Learning (ICML'16). P449– 457

• L. Wang and Z.-H. Zhou. Cost-Saving effect of crowdsourcing learning. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). P2111- 2117

• X. Huo, M. Li and Z.-H. Zhou. Learning unified features from natural and programming languages for locating buggy source codes. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). P1606-1612

• Y. Yang, D.-C. Zhan and Y. Jiang. Learning by actively querying strong modal features. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). P2280-2286

• H. Qian, Y.-Q. Hu and Y. Yu. Derivative-Free optimization of high-dimensional non-convex Functions by sequential random embeddings. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). P1946-1952

• W.-J. Li, S. Wang and W.-C. Kang. Feature learning based deep supervised hashing with pairwise labels. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). P1711-1717

• Y.-F. Li, S.-B. Wang and Z.-H. Zhou. Graph quality judgement: a Large margin expedition. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). P1725-1731

• L. Liu, T. Dietterich, N. Li and Z.-H. Zhou. Transductive optimization of top k precision. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). P1781-1787

• C. Qian, J. Shi, Y. Yu, K. Tang and Z.-H. Zhou. Parallel pareto optimization for subset selection. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16). P1939-1945

• Y. Yu, P.-F. Hou, Q. Da and Y. Qian. Boosting nonparametric policies. In: Proceedings of the 2016 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'16). P477- 484

• X. Liu, C. Aggarwal, Y.-F. Li, X. Kong, X. Sun and S. Sathe. Kernelized matrix factorization for collaborative filtering. In: Proceedings of SIAM International Conference on Data Mining (SDM'16). P399-416

• H.-P. Lu, J.-X. Wu and Y. Zhang. Learning Compact Binary Codes from Higher-Order Tensors via Free-Form Reshaping and Binarized Multilinear PCA. In: Proceedings of the Annual International Joint Conference on Neural Networks (IJCNN'16). P3008-3015

• Z.-C. Huang and D.-C. Zhan. Positive Instance Detection based Multi-Instance Learning via Linearly Localized Interpolation. In: Proceedings of the 2016 International Conference on Intelligence Science and Big Data Engineering (ISCIDE'16).

• W.-H. Zheng and M. Li. Exploiting heterogeneous data on software development Q&A forum for best answer prediction. In: Proceedings of the 2016 International Conference on Intelligence Science and Big Data Engineering (ISCIDE'16).

• W. Gao, L. Wang, Y.-F. Li and Z.-H. Zhou. Risk minimization in the presence of label noise. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P1575-1581

• D.-C. Zhan, P. Hu, Z. Chu and Z.-H. Zhou. Learning expected hitting time distance. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P2309-2314

• W.-C. Kang, W.-J. Li and Z.-H. Zhou. Column sampling based discrete supervised hashing. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P1230-1236

• H.-J. Ye, D.-C. Zhan, and Y. Jiang. Instance specific metric subspace learning: A bayesian approach. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P2272-2278

• Y. Yu, H. Qian and Y.-Q. Hu. Derivative-free optimization via classification. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P2286-2292

• H. Qian and Y. Yu. Scaling simultaneous optimistic optimization for high-dimensional non-convex functions with low effective dimensions. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P2000-2006

• S.-Y. Zhao and W.-J. Li. Fast asynchronous parallel stochastic gradient descent: A lock-free approach with convergence guarantee. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P2379-2385

• Y.-F. Li, J. Kwok, and Z.-H. Zhou. Towards safe semi-supervised learning for multivariate performance measures. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P1816-1822

• L.-J. Zhang, T. Yang, J. Yi, R. Jin and Z.-H. Zhou. Stochastic optimization for kernel PCA. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P2315-2322

• J.-X. Wu, B.-B. Gao and G. Liu. Representing sets of instances for visual recognition. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P2237-2243

• Z. Li, T. Yang, L.-J. Zhang, and R. Jin. Fast and accurate refined nystrom based kernel SVM. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P1830-1836

• Y.-T. Qiang, Y. Fu, Y. Guo, Z.-H. Zhou and L. Sigal. Learning to generate posters of scientic papers. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P51-57

• W. Zhang, L.-J. Zhang, R. Jin, D. Cai and X. He. Accelerated sparse linear regression via random projection. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16). P2337-2343

Top

Journal Article and Book

• 周志华*《机器学习》.清华大学出版.中文专著 ISBN: 978-7 -302-42328-7

• Y.-H. Zhou and Z.-H. Zhou. Large margin distirbution learning with cost interval and unlabeled data. IEEE Transactions on Knowledge and Data Engineering. Vol. 28 No.7 P1749-1763

• W. Gao, L. Wang, R. Jin. S. Zhu and Z.-H. Zhou. One-Pass AUC optimization. Artificial Intelligence. Vol. 236 P1-29

• X. He, C. Zhang, L.-J. Zhang, and X. Li. A-Optimal Projection for Image Representation. IEEE Transactions on Pattern Analysis & Machine Intelligence. Vol.38 No.5 P1009-1015

• Y. Zhang, L. Cheng, J. Wu, J. Cai, M. Do and J. Lu. Action recognition in still images with minimum annotation efforts. IEEE Transactions on Image Processing. Vol. 25 No. 11 P5479-5490

• Y. Zhang, J. Wu and J. Cai. Compact representation of high-dimensional feature vectors for large-scale image recognition and retrieval. IEEE Transactions on Image Processing. Vol. 25 No. 5 P2407-2419.

• W. Lin, Y. Mi, W. Wang, J. Wu, J. Wang and T. Mei. A diffusion and clustering-based approach for finding coherent motions and understanding crowd scenes. IEEE Transactions on Image Processing. Vol. 25 No. 4 P1674-1687

• Y. Zhang, X. Wei, J. Wu, J. Cai, J. Lu, V. Nguyen and M. Do. Weakly Supervised Fine-Grained Categorization with Part-Based Image Representation. IEEE Transactions on Image Processing. Vol. 25 No.4 P1713-1725.

• X. Wei and Z.-H. Zhou. An empirical study on image bag generators for multi-instance learning. Machine Learning. Vol. 105 No. 2 P155-198

• W. Gao and Z.-H. Zhou. Dropout radermacher complexity of deep neural networks. Science China: Information Sciences. Vol. 59 Article 12

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