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Publication History 2016-2018

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

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

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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

  • J. Wu, Y. Zhang and W. Lin. Good practices for learning to recognize actions using FV and VLAD. IEEE Transactions on Cybernetics. Vol. 46 No. 12 P2978-2990.

  • Y. Fu, H. Xiong, Y. Ge, Y. Zheng, Z. Yao, and Z.-H. Zhou. Modeling of geographical dependencies for real estate appraisal. ACM Transactions on Knowledge Discovery from Data. Vol. 11 No. 1 Article 11

  • Z.-H. Zhou. Learnware: On the future of machine learning. Frontiers of Computer Science. Vol. 10 No. 4 P589-590

  • G. Zhou, J. Wu, C. Zhang and Z.-H. Zhou. Minimal gated unit for recurrent neural networks. International Journal of Automation and Computing. Vol. 13 No. 3 P226-234.

  • C. Qian, Y. Yu, and Z.-H. Zhou. Analyzing evolutionary optimization in noisy environments. Evolutionary Computation, in press. (CORR abs/1311.4987)

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