qianc

Chao Qian (钱 超)

Associate Researcher
School of Computer Science and Technology
University of Science and Technology of China (USTC)

Office: 607, West Lab Building of Science and Technology
Address: 96 Jinzhai Road, Hefei, Anhui, China, 230027
Email: chaoqian@ustc.edu.cn

Short Biography

Research Interest

My research interests include artificial intelligence, machine learning and evolutionary algorithms. Now I am working on

  • Theoretical analysis of evolutionary algorithms
  • Designing evolutionary algorithms with provable approximation guarantee for sophisticated optimization problems
  • Recent News

  • I am chair of IEEE Computational Intelligence Society (CIS) Task Force "Theoretical Foundations of Bio-inspired Computation"
  • I (together with Yang Yu) will give a tutorial "Pareto Optimization for Subset Selection: Theory and Applications in Machine Learning" at WCCI 2018
  • I gave an invited talk "Subset Selection by Pareto Optimization: Theories and Practical Algorithms" at ECOLE 2018 (第五届演化计算与学习研讨会) [Slides]
  • I was guest co-editor (together with Yang Yu) of a Special Issue of Journal of Software entitled "Evolutionary Learning" (软件学报演化学习专刊)
  • I gave a tutorial "An Introduction on Theory of Evolutionary Optimization" at ECOLE 2016 (第三届演化计算与学习研讨会) [Slides]
  • I was invited to attend the Dagstuhl Seminar "Theory of Randomized Optimization Heuristics"
  • Publications

    Technical Report

    1. Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Yang Yu, and Chao Qian. ZOOpt: Toolbox for Derivative-Free Optimization.
      CORR abs/1801.00329, 2018. [PDF]

    2. Chao Qian, Yang Yu, Ke Tang, Xin Yao, and Zhi-Hua Zhou. Maximizing Non-monotone/Non-submodular Functions by Multi-objective Evolutionary Algorithms.
      CORR abs/1711.07214, 2017. [PDF]

    3. Chao Qian, Chao Bian, Wu Jiang, and Ke Tang. Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise.
      CORR abs/1711.00956, 2017. [PDF] (Extension of GECCO'17)

    Journal Article

    1. Chao Qian, Jing-Cheng Shi, Ke Tang, and Zhi-Hua Zhou. Constrained Monotone k-Submodular Function Maximization Using Multi-objective Evolutionary Algorithms with Theoretical Guarantee.
      IEEE Transactions on Evolutionary Computation, in press. [Preprint PDF] [Supplementary] [PDF](code)

    2. 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][PDF]

    3. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Analyzing Evolutionary Optimization in Noisy Environments.
      Evolutionary Computation, 2018, 26(1): 1-41. [Preprint PDF] [PDF]

    4. 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] [PDF]

    5. 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] [PDF]

    6. Chao Qian, Yang Yu, and Zhi-Hua Zhou. An Analysis on Recombination in Multi-Objective Evolutionary Optimization.
      Artificial Intelligence, 2013, 204: 99-119. [Preprint PDF] [PDF]

    Conference Paper

    1. Chao Bian, Chao Qian, and Ke Tang. Towards a Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under General Bit-wise Noise.
      In: Proceedings of the 15th International Conference on Parallel Problem Solving from Nature (PPSN'18), Coimbra, Portugal, 2018, to appear. [PDF]

    2. Chao Bian, Chao Qian, and Ke Tang. A General Approach to Running Time Analysis of Multi-objective Evolutionary Algorithms.
      In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, to appear. [PDF]

    3. Chao Qian, Yang Yu, and 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, to appear. [PDF]

    4. Chao Qian, Chao Feng, and Ke Tang. Sequence Selection by Pareto Optimization.
      In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, to appear. [PDF]

    5. Chao Qian, Guiying Li, Chao Feng, and Ke Tang. Distributed Pareto Optimization for Subset Selection.
      In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, to appear. [PDF]

    6. Guiying Li, Chao Qian, Chunhui Jiang, Xiaofen Lu, and Ke Tang. Optimization based Layer-wise Magnitude-based Pruning for DNN Compression.
      In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, to appear. [PDF]

    7. Chunhui Jiang, Guiying Li, Chao Qian, and Ke Tang. Efficient DNN Neuron Pruning by Minimizing Layer-wise Nonlinear Reconstruction Error.
      In: Proceedings of the 27th International Joint Conference on Artificial Intelligence (IJCAI'18), Stockholm, Sweden, 2018, to appear. [PDF]

    8. 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, to appear. [PDF with Appendix]

    9. Chao Qian, Yibo Zhang, Ke Tang, and Xin Yao. On Multiset Selection with Size Constraints.
      In: Proceedings of the 32nd AAAI Conference on Artificial Intelligence (AAAI'18), New Orleans, LA, 2018, pp.1395-1402. [PDF] [Supplementary](code)

    10. 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, pp.3563-3573. [PDF] [Supplementary](code)

    11. Chunhui Jiang, Guiying Li, and Chao Qian. Dynamic and Adaptive Threshold for DNN Compression from Scratch.
      In: Proceedings of the 11th International Conference on Simulated Evolution and Learning (SEAL'17), Shenzhen, China, 2017, pp.858-869. [PDF]

    12. 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)

    13. 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)

    14. Chao Qian, Chao Bian, Wu Jiang, and Ke Tang. Running Time Analysis of the (1+1)-EA for OneMax and LeadingOnes under Bit-wise Noise.
      In: Proceedings of the 19th ACM Conference on Genetic and Evolutionary Computation (GECCO'17), Berlin, Germany, 2017, pp.1399-1406. [PDF]

    15. 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, pp.2488-2493. [PDF]

    16. 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]
      (Best Paper Award)

    17. Chao Qian, Ke Tang, and Zhi-Hua 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), Edinburgh, Scotland, 2016, pp.835-846. [PDF]

    18. 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](code)

    19. Bingdong Li, Chao Qian, Jinlong Li, Ke Tang, and Xin Yao. Search Based Recommender System Using Many-Objective Evolutionary Algorithm.
      In: Proceedings of the 2016 IEEE Congress on Evolutionary Computation (CEC'16), Vancouver, Canada, 2016, pp.120-126. [PDF]

    20. 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, pp.1765-1773. [PDF](code)

    21. 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]

    22. Yang Yu and 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. [PDF]

    23. 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] [Long Version](code)

    24. 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]

    25. 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]

    26. 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]
      (Best Theory Paper Award)

    27. 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), Dublin, Ireland, 2011, pp.265-266. [PDF] (poster)

    28. 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), Krakow, Poland, 2010, pp.144-153. [PDF]

    Native Paper

    1. 俞扬, 钱超. 演化学习专刊前言. 软件学报, 2018, 29(9). [PDF]

    2. 钱超. 多目标演化学习理论与方法研究. 中国人工智能学会通讯, 2017, 7(9): 20-29. [PDF]

    3. 钱超, 周志华. 基于分解策略的多目标演化子集选择算法. 中国科学: 信息科学, 2016, 46(9): 1276-1287. [PDF]

    4. 钱超, 俞扬. 演化学习研究进展. 中国人工智能学会通讯, 2016, 6(8): 7-12. [PDF]

    5. 钱超, 俞扬. 机器学习顶级会议NIPS 2015. 中国计算机学会通讯, 2016, 12(6): 80-82. [PDF]

    Awards

    Professional Services