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

2017

[Conference Paper][Journal Article]

Conference Paper

  • 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.

  • 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.

  • 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.

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Journal Article and Book
  • 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.

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