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Manuscripts

  • Jing-Cheng Shi, Yang Yu, Qing Da, Shi-Yong Chen, An-Xiang Zeng. Virtual-Taobao: Virtualizing real-world online retail environment for reinforcement learning. arXiv:1805.10000, 2018.

  • Wang-Zhou Dai, Qiu-Ling Xu, Yang Yu, and Zhi-Hua Zhou. Tunneling Neural Perception and Logic Reasoning through Abductive Learning. arXiv:1802.01173, 2018.

  • Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Yang Yu, and Chao Qian. ZOOpt: Toolbox for Derivative-Free Optimization. arXiv:1801.00329, 2018.

Book Chapter

  • Zhi-Hua Zhou and Yang Yu. The AdaBoost algorithm. In: X. Wu and V. Kumar eds. The Top Ten Algorithms in Data Mining, Boca Raton, FL: Chapman & Hall, 2009. (PDF)

Conference Papers

  • Ji Feng, Yang Yu, Zhi-Hua Zhou. Multi-layered gradient boosting decision trees. In: Advances in Neural Information Processing Systems 31 (NIPS'18), Montreal, Canada, 2018. arXiv:1806.00007.

  • Yang Yu. Towards Sample Efficient Reinforcement Learning. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18) (Early Career), Stockholm, Sweden, 2018, pp.5739-5743. (PDF)

  • Jorge G. Madrid, Hugo Jair Escalante, Eduardo F. Morales, Wei-Wei Tu, Yang 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.

  • Shi-Yong Chen, Yang Yu, Qing Da, Jun Tan, Hai-Kuan Huang and Hai-Hong Tang. Stabilizing 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. (PDF)

  • Yujing Hu, Qing Da, Anxiang Zeng, Yang Yu and Yinghui 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. (PDF)

  • Chao Qian, Chao Bian, Yang Yu, Ke Tang, and Xin 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. (PDF)

  • Yang Yu, Wen-Ji Zhou. Mixture of GANs for clustering. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, pp.3047-3053. (PDF)

  • Chao Zhang, Yang Yu, Zhi-Hua 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, pp.3061-3067. (PDF)

  • Yi-Qi Hu, Yang Yu, Zhi-Hua 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, pp.2276-2282. (PDF)

  • Chao Qian, Yang Yu, Ke 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, pp.1478-1484. (PDF)

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

  • Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, and Zhi-Hua Zhou. Subset selection under noise. In: Advances in Neural Information Processing Systems 30 (NIPS'17), Long Beach, CA, 2017. (PDF with Appendix)

  • 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. (PDF)

  • Yang Yu, Wei-Yang Qu, Nan Li, and Zimin Guo. Open category classification by adversarial sample generation. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3357-3363. (PDF)(Code)

  • Wen-Ji Zhou, Yang Yu, and Min-Ling Zhang. Binary linear compression for multi-label classification. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3546-3552. (PDF)

  • Jing-Wen Yang, Yang Yu, and Xiao-Peng Zhang. Life-stage modeling by customer-manifold embedding. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.3259-3265. (PDF)

  • Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang and Zhi-Hua Zhou. Optimizing ratio of monotone set functions. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.2606-2612. (PDF) (Code)

  • Chao Qian, Jing-Cheng Shi, Yang Yu, and Ke Tang. On subset selection with general cost constraints. In: Proceedings of the 26th International Joint Conference on Artificial Intelligence (IJCAI'17), Melbourne, Australia, 2017, pp.2613-2619. (PDF) (Code)

  • Jianbing Zhang, Yixin Sun, Shu-Jian Huang, Cam-Tu Nguyen, Xiaoliang Wang, Xin-Yu Dai, Jiajun Chen, and Yang 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, pp.4221-4227. (PDF) (online demo)

  • Hong Qian and Yang 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, pp.875-881. (PDF with Appendix)

  • Yi-Qi Hu, Hong Qian, and Yang 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, pp.2029-2035. (PDF with Appendix) (Code)

  • Chao Qian, Yang Yu, and Zhi-Hua Zhou. A lower bound analysis of population-based evolutionary algorithms for pseudo-Boolean functions. In: Proceedings of the 17th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL'16), Yangzhou, China, 2016, pp.457-467. (PDF) (This paper won a Best Paper Award of IDEAL'16)

  • Xin Li, Yongjuan Liang, Hong Qian, Yi-Qi Hu, Lei Bu, Yang Yu, Xin Chen, and Xuandong Li. Symbolic execution of complex program driven by machine learning based constraint solving. In: Proceedings of the 31th IEEE/ACM International Conference on Automated Software Engineering (ASE'16), Singapore, 2016, pp.554-559. (PDF)

  • Han Wang and Yang Yu. Exploring multi-action relationship in reinforcement learning. In: Proceedings of the 14th Pacific Rim International Conference on Artificial Intelligence (PRICAI'16), Phuket, Thailand, 2016, pp.574–587. (PDF)

  • Hong Qian, Yi-Qi Hu and Yang 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), New York, NY, 2016, pp.1946-1952. (PDF) (Code)

  • Chao Qian, Jing-Cheng Shi, Yang Yu, Ke Tang, and Zhi-Hua Zhou. Parallel Pareto optimization for subset selection. In: Proceedings of the 25th International Joint Conference on Artificial Intelligence (IJCAI'16), New York, NY, 2016, pp.1939-1945. (PDF)

  • Hong Qian and Yang Yu. On sampling-and-classification optimization in discrete domains. In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC'16), Vancouver, Canada, 2016, pp.4374-4381. (PDF)

  • Yang Yu, Peng-Fei Hou, Qing Da, and Yu Qian. Boosting nonparametric policies. In: Proceedings of the 2016 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'16), Singapore, 2016, pp.477-484. (PDF) (Code)

  • Yang Yu, Hong Qian, and Yi-Qi Hu. Derivative-free optimization via classification. In: Proceedings of the 30th AAAI Conference on Artificial Intelligence (AAAI'16), Phoenix, AZ, 2016, pp.2286-2292. (PDF) (Appendix) (Code)

  • Hong Qian, Yang 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), Phoenix, AZ, 2016, pp.2000-2006. (PDF)

  • Chao Qian, Yang Yu and Zhi-Hua Zhou. Subset selection by Pareto optimization. In: Advances in Neural Information Processing Systems 28 (NIPS'15) , Montreal, Canada, 2015. (PDF) (code)

  • Chao Qian, Yang Yu and Zhi-Hua Zhou. On constrained Boolean Pareto optimization. In: Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI'15), Buenos Aires, Argentina, 2015, pp.389-395. (PDF)

  • Yang Yu, Chao Qian. Running time analysis: Convergence-based analysis reduces to switch analysis. In: Proceedings of the 2015 IEEE Congress on Evolutionary Computation (CEC'15), Sendai, Japan, 2015, pp.2603-2610, pp.2603-2610. (PDF)

  • Chao Qian, Yang Yu and Zhi-Hua Zhou. Pareto ensemble pruning. In: Proceedings of the 29th AAAI Conference on Artificial Intelligence (AAAI'15), Austin, TX, 2015, pp.2935-2941.(PDF)

  • Chao Qian, Yang Yu, Yaochu Jin and Zhi-Hua Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. In: Proceedings of the 13th International Conference on Parallel Problem Solving from Nature (PPSN’14), Ljubljana, Slovenia, 2014, pp.302-311. (PDF)

  • Yang Yu and Qing Da, PolicyBoost: Functional policy gradient with ranking-based reward objective. In: Proceedings of AAAI Workshop on AI and Robotics (AIRob'14), Quebec City, Canada, 2014, pp.57-62. (PDF)

  • Yang Yu, and Hong Qian. The sampling-and-learning framework: A statistical view of evolutionary algorithms. In: Proceedings of the 2014 IEEE Congress on Evolutionary Computation (CEC'14), Beijing, China, 2014, pp.149-158. (PDF)

  • Qing Da, Yang Yu, and Zhi-Hua Zhou. Learning with augmented class by exploiting unlabeled data. In: Proceedings of the 28th AAAI Conference on Artificial Intelligence (AAAI'14), Québec city, Canada, 2014, pp.1760-1766. (PDF)

  • Qing Da, Yang Yu, and Zhi-Hua Zhou. Napping for functional representation of policy. In: Proceedings of the 2014 International Conference on Autonomous Agents and Multi-Agent Systems (AAMAS'14), Paris, France, 2014, pp.189-196. (PDF) (Code)

  • Qing Da, Yang Yu, and Zhi-Hua Zhou. Self-practice imitation learning from weak policy. In: Proceedings of the 2nd IAPR International Workshop on Partially Supervised Learning (PSL'13), Nanjing, China, 2013, pp.9-20.

  • Yang Yu, Xin Yao, and Zhi-Hua Zhou. On the approximation ability of evolutionary optimization with application to minimum set cover: Extended abstract. In: Proceedings of the 23rd International Joint Conference on Artificial Intelligence (IJCAI'13) (Journal Paper Track), Beijing, China, 2013.

  • Nan Li, Yang Yu, and Zhi-Hua Zhou. Diversity regularized ensemble pruning. In: Proceedings of the 23rd European Conference on Machine Learning (ECML'12), Bristol, U.K., 2012, pp.330-345. (PDF) (code)

  • Chao Qian, Yang Yu, and Zhi-Hua Zhou. On algorithm-dependent boundary case identification for problem classes. In: Proceedings of the 12th International Conference on Parallel Problem Solving from Nature (PPSN'12) Taormina, Italy, 2012, pp.62-71. (PDF)

  • Sheng-Jun Huang, Yang Yu, and Zhi-Hua Zhou. Multi-label hypothesis reuse. In: Proceedings of the 18th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD'12), Beijing, China, 2012, pp.525-533. (PDF) (code) (The poster presentation won the Best Poster Award at KDD'12)

  • Sheng-Jun Huang, Yang Yu and Zhi-Hua Zhou, Multi-label boosting via hypothesis reuse. In: Proceedings of NIPS Workshop on Chanllenges in Learning Hierarchical Models: Transfer Learning and Optimization, Granada, Spain 2011.

  • Wang-Zhou Dai, Yang Yu, and Zhi-Hua Zhou. Lifted-rollout for approximate policy iteration of Markov decision process. In: Proceedings of the International Workshop on Learning and Data Mining for Robotics (LEMIR'11), in conjunction with ICDM'11, Vancouver, Canada, 2011.

  • Yang Yu, Yu-Feng Li, and Zhi-Hua Zhou. Diversity Regularized Machine In: Proceedings of the 22nd International Joint Conference on Artificial Intelligence (IJCAI'11), Barcelona, Spain, 2011, pp. 1603-1608. (PDF) (code)

  • Chao Qian, Yang Yu, and Zhi-Hua Zhou. An analysis on recombination in multi-objective evolutionary optimization. In: Proceedings of the 13th ACM Conference on Genetic and Evolutionary Computation (GECCO'11), Dublin, Ireland, 2011, pp. 2051-2058. (PDF) (This paper won the Best Paper Award of the Theory Track at GECCO'11)

  • Chao Qian, Yang Yu, and Zhi-Hua Zhou. Collisions are helpful for computing unique input-output sequences. In: Proceedings of the 13th ACM Conference on Genetic and Evolutionary Computation (GECCO'11) (Companion Material/Poster), Dublin, Ireland, 2011, pp. 265-266. (PDF)

  • Yang Yu, Chao Qian, and Zhi-Hua Zhou. Towards analyzing recombination operators in evolutionary search. In: Proceedings of the 11th International Conference on Parallel Problem Solving from Nature (PPSN'10) Part I, Krakow, Poland, 2010, pp.144-153. (PDF)

  • Nan Li, Yang Yu, and Zhi-Hua Zhou. Semi-naive exploitation of one-dependence estimators. In: Proceedings of the 9th IEEE International Conference on Data Mining (ICDM'09), Miami, FL, 2009, pp.278-287. (PDF)

  • Yang Yu and Zhi-Hua Zhou. A framework for modeling positive class expansion with single snapshot. In: Proceedings of the 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'08), Osaka, Japan, LNAI 5012, 2008, pp.429-440. (PDF) (slides) (This paper won the Best Paper Award at PAKDD'08)

  • Yang Yu and Zhi-Hua Zhou. On the usefulness of infeasible solutions in evolutionary search: A theoretical study. In: Proceedings of the IEEE Congress on Evolutionary Computation (CEC'08), Hong Kong, China, 2008, pp.835-840. (PDF)

  • Li-Ping Liu, Yang Yu, Yuan Jiang, and Zhi-Hua Zhou. TEFE: A time-efficient approach to feature extraction. In: Proceedings of the 8th IEEE International Conference on Data Mining (ICDM'08), Pisa, Italy, 2008, pp.423-432. (PDF)

  • Yang Yu, Zhi-Hua Zhou, and Kai Ming Ting. Cocktail ensemble for regression. In: Proceedings of the 7th IEEE International Conference on Data Mining (ICDM'07), Omaha, NE, 2007, pp.721-726. (PDF)

  • Yang Yu and Zhi-Hua Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. In: Proceedings of the 21st National Conference on Artificial Intelligence (AAAI'06), Boston, MA, 2006, pp.555-560. (PDF)

Journal Articles

  • Yang Yu, Shi-Yong Chen, Qing Da, Zhi-Hua Zhou. Reusable reinforcement learning via shallow trails. IEEE Transactions on Neural Networks and Learning Systems, 2018, 29(6): 2204-2215.(Preprint PDF)

  • Chao Qian, Yang Yu, Ke Tang, Yaochu Jin, Xin Yao, and Zhi-Hua Zhou. On the effectiveness of sampling for evolutionary optimization in noisy environments. Evolutionary Computation, 2018, 26(2): 237-267. (Preprint PDF) (Supplementary)

  • Chao Qian, Yang Yu, and Zhi-Hua Zhou. Analyzing evolutionary optimization in noisy environments. Evolutionary Computation, 2018, 26(1): 1–41. (Preprint PDF)

  • Yang Yu, Chao Qian, and Zhi-Hua Zhou. Switch analysis for running time analysis of evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 2015, 19(6):777-792. (Preprint PDF)

  • Chao Qian, Yang Yu, and Zhi-Hua Zhou. Variable solution structure can be helpful in evolutionary optimization. Science China: Information Sciences, 2015, 58(11): 1-17. (Preprint PDF)


  • Chao Qian, Yang Yu, and Zhi-Hua Zhou. An analysis on recombination in multi-objective evolutionary optimization. Artificial Intelligence, 2013, 204:99-119. (Extended from GECCO'11) (Preprint PDF)

  • Yang Yu, Xin Yao, and Zhi-Hua Zhou. On the approximation ability of evolutionary optimization with application to minimum set cover. Artificial Intelligence, 2012, 180-181:20-33. (Preprint PDF) (CORR abs/1011.4028)

  • Yang Yu and Zhi-Hua Zhou. A framework for modeling positive class expansion with single snapshot. Knowledge and Information Systems, 2010, 25(2):211-227. (Extended from PAKDD'08) (Preprint PDF) (slides) (code&data)

  • Yang Yu and Zhi-Hua Zhou. A new approach to estimating the expected first hitting time of evolutionary algorithms. Artificial Intelligence, 2008, 172(15): 1809-1832. (Extended from AAAI'06) (Preprint PDF)

  • Fei Tony Liu, Kai Ming Ting, Yang Yu, and Zhi-Hua Zhou . Spectrum of variable-random trees. Journal of Artificial Intelligence Research, 2008, 32:355-384. (Preprint PDF)

  • Yang Yu, De-Chuan. Zhan, Xu-Ying Liu, Ming Li, and Zhi-Hua Zhou. Predicting future customers via ensembling gradually expanded trees. International Journal of Data Warehousing and Mining, 2007 3(2): 12-21. (Invited paper for the PAKDD'06 Data Mining Competition (Open Category) Grand Champion Team) (Preprint PDF)

  • Zhi-Hua Zhou and Yang Yu. Ensembling local learners through multi-modal perturbation. IEEE Transactions on System, Man, And Cybernetics - Part B: Cybernetics, 2005, 35(4): 725-735. (Preprint PDF) (code)

  • Zhi-Hua Zhou and Yang Yu. Adapt bagging to nearest neighbor classifiers. Journal of Computer Science and Technology, 2005, vol.20, no.1 pp.48-54. (Preprint PDF) (detailed result)

Thesis

  • Yang Yu. Evolutionary Computation: Theoretical Analysis and Learning Algorithms. Ph.D. Dissertation, 2011.

  • Yang Yu. Local Validity Based Selective Ensemble of Decision Trees. B.Sc. Thesis, 2004. (in Chinese with English abstract) (PDF)
The end