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Publication History 2017

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[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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