Chao Qian (钱 超)

副教授,博导,国家优青
Associate Professor
School of Artificial Intelligence
Nanjing University

Office: Room A502, International College Area, Xianlin Campus
Address:163 Xianlin Avenue, Nanjing, Jiangsu, China, 210023
Email: qianc at nju dot edu dot cn, qianc at lamda dot nju dot edu dot cn


Short Biography



Research Interest

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

Recent News

Publications

Book

  1. Zhi-Hua Zhou, Yang Yu, and Chao Qian. Evolutionary Learning: Advances in Theories and Algorithms. Singapore: Springer, 2019. (ISBN 978-981-13-5955-2)

  2. 周志华, 俞扬, 钱超 著. 演化学习: 理论与算法进展, 北京: 人民邮电出版社, 2021. (ISBN 978-7-115-55803-9)

Journal Article (* indicates that I am the corresponding author)

  1. Guiying Li, Peng Yang, Chao Qian, Richang Hong, and Ke Tang. Stage-wise Magnitude-based Pruning for Recurrent Neural Networks.
    IEEE Transactions on Neural Networks and Learning Systems, in press. [PDF]

  2. Yu-Ren Liu, Yi-Qi Hu, Hong Qian, Yang Yu, and Chao Qian. ZOOpt: Toolbox for Derivative-Free Optimization.
    Science China: Information Sciences, in press.

  3. Chao Qian, Dan-Xuan Liu, and Zhi-Hua Zhou. Result Diversification by Multi-objective Evolutionary Algorithms with Theoretical Guarantees.
    Artificial Intelligence, 2022, 309: 103737. [Preprint PDF][PDF]

  4. Chao Qian. Multi-objective Evolutionary Algorithms are Still Good: Maximizing Monotone Approximately Submodular Minus Modular Functions.
    Evolutionary Computation, 2021, 29(4): 463–490. [Preprint PDF][PDF]

  5. Chao Qian, Chao Bian, Yang Yu, Ke Tang, and Xin Yao. Analysis of Noisy Evolutionary Optimization When Sampling Fails.
    Algorithmica, 2021, 83(4): 940-975. [Preprint PDF][PDF]

  6. Wenjing Hong, Chao Qian*, and Ke Tang. Efficient Minimum Cost Seed Selection with Theoretical Guarantees for Competitive Influence Maximization.
    IEEE Transactions on Cybernetics, 2021, 51(12): 6091-6104. [Preprint PDF][PDF](code)

  7. Chao Bian, Chao Qian*, Yang Yu, and Ke Tang. On the Robustness of Median Sampling in Noisy Evolutionary Optimization.
    Science China: Information Sciences, 2021, 64(5): 1-13. [Preprint PDF][PDF]

  8. Chao Bian, Chao Qian*, Ke Tang, and Yang Yu. Running Time Analysis of the (1+1)-EA for Robust Linear Optimization.
    Theoretical Computer Science, 2020, 843: 57-72. [Preprint PDF][PDF]

  9. Chao Qian. Distributed Pareto Optimization for Large-scale Noisy Subset Selection.
    IEEE Transactions on Evolutionary Computation, 2020, 24(4): 694-707. [Preprint PDF][PDF](code)

  10. Chao Qian, Yang Yu, Ke Tang, Xin Yao, and Zhi-Hua Zhou. Maximizing Submodular or Monotone Approximately Submodular Functions by Multi-objective Evolutionary Algorithms.
    Artificial Intelligence, 2019, 275: 279-294. [Preprint PDF][PDF]

  11. 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.
    Algorithmica, 2019, 81(2): 749-795. [Preprint PDF][PDF]

  12. 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, 2018, 22(4): 595-608. [Preprint PDF] [Supplementary] [PDF](code)

  13. 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]

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

  15. 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]

  16. 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]

  17. 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 (* indicates my student)

  1. Chao Bian* and Chao Qian. Better Running Time of the Non-dominated Sorting Genetic Algorithm II (NSGA-II) by Using Stochastic Tournament Selection.
    In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), Dortmund, Germany, 2022, to appear. [PDF]

  2. Zi-An Zhang*, Chao Bian, and Chao Qian. Running Time Analysis of the (1+1)-EA using Surrogate Models on OneMax and LeadingOnes.
    In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), Dortmund, Germany, 2022, to appear. [PDF]

  3. Yu-Chang Wu*, Yi-Xiao He, Chao Qian, and Zhi-Hua Zhou. Multi-objective Evolutionary Ensemble Pruning Guided by Margin Distribution.
    In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), Dortmund, Germany, 2022, to appear. [PDF](code)

  4. Jia-Liang Wu*, Haopu Shang, Wenjing Hong, and Chao Qian. Robust Neural Network Pruning by Cooperative Coevolution.
    In: Proceedings of the 17th International Conference on Parallel Problem Solving from Nature (PPSN'22), Dortmund, Germany, 2022, to appear. [PDF](code)

  5. Chao Qian. Towards Theoretically Grounded Evolutionary Learning.
    In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), Vienna, Austria, 2022, to appear. [PDF]
    (Early Career Paper)

  6. Haopu Shang*, Jia-Liang Wu, Wenjing Hong, and Chao Qian. Neural Network Pruning by Cooperative Coevolution.
    In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), Vienna, Austria, 2022, to appear. [PDF](code)

  7. Chao Bian*, Yawen Zhou, and Chao Qian. Robust Subset Selection by Greedy and Evolutionary Pareto Optimization.
    In: Proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI'22), Vienna, Austria, 2022, to appear. [PDF](code)

  8. Yutong Wang*, Ke Xue, and Chao Qian. Evolutionary Diversity Optimization with Clustering-based Selection for Reinforcement Learning.
    In: Proceedings of the 10th International Conference on Learning Representations (ICLR'22), Virtual, 2022. [PDF](code)

  9. Ke Xue*, Chao Qian, Ling Xu, and Xudong Fei. Evolutionary Gradient Descent for Non-convex Optimization.
    In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Virtual, 2021, pp.3221-3227. [PDF](code)

  10. Chao Bian*, Chao Qian, Frank Neumann, and Yang Yu. Fast Pareto Optimization for Subset Selection with Dynamic Cost Constraints.
    In: Proceedings of the 30th International Joint Conference on Artificial Intelligence (IJCAI'21), Virtual, 2021, pp.2191-2197. [PDF]

  11. Fei-Yu Liu* and Chao Qian. Prediction Guided Meta-Learning for Multi-Objective Reinforcement Learning.
    In: Proceedings of the 2021 IEEE Congress on Evolutionary Computation (CEC'21), Krakow, Poland, 2021, pp.2171-2178. [PDF]
    (Best Student Paper Award Nomination)

  12. Chao Feng* and Chao Qian. Multi-objective Submodular Maximization by Regret Ratio Minimization with Theoretical Guarantee.
    In: Proceedings of the 35th AAAI Conference on Artificial Intelligence (AAAI'21), Virtual, 2021, pp.12302-12310. [PDF](code)

  13. Fei-Yu Liu*, Zi-Niu Li, and Chao Qian. Self-Guided Evolution Strategies with Historical Estimated Gradients.
    In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), Yokohama, Japan, 2020, pp.1474-1480. [PDF](code)

  14. Chao Qian, Hang Xiong, and Ke Xue. Bayesian Optimization using Pseudo-Points.
    In: Proceedings of the 29th International Joint Conference on Artificial Intelligence (IJCAI'20), Yokohama, Japan, 2020, pp.3044-3050. [PDF with Appendix]

  15. Chao Qian, Chao Bian, and Chao Feng. Subset Selection by Pareto Optimization with Recombination.
    In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020, pp.2408-2415. [PDF with Appendix](code)

  16. Chao Bian*, Chao Feng, Chao Qian, and Yang Yu. An Efficient Evolutionary Algorithm for Subset Selection with General Cost Constraints.
    In: Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI'20), New York, NY, 2020, pp.3267-3274. [PDF](code)

  17. Chao Feng*, Chao Qian, and Ke Tang. Unsupervised Feature Selection by Pareto Optimization.
    In: Proceedings of the 33rd AAAI Conference on Artificial Intelligence (AAAI'19), Honolulu, HI, 2019, pp.3534-3541. [PDF](code)

  18. 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, pp.165-177. [PDF]

  19. 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, pp.1405-1411. [PDF]

  20. 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, pp.1478-1484. [PDF]

  21. 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, pp.1485-1491. [PDF](code)

  22. 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, pp.1492-1498. [PDF](code)

  23. 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, pp.2383-2389. [PDF]

  24. 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, pp.2298-2304. [PDF]

  25. 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, pp.1507-1514. [PDF with Appendix]

  26. 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)

  27. 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)

  28. 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]

  29. 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)

  30. 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)

  31. 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]

  32. 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]

  33. 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)

  34. 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]

  35. 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)

  36. 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]

  37. 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)

  38. 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]

  39. 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]

  40. 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)

  41. 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]

  42. 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]

  43. 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)

  44. 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)

  45. 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]

Technical Report (* indicates my student)

  1. Chao Qian, Dan-Xuan Liu, Chao Feng, and Ke Tang. Multi-objective Evolutionary Algorithms are Generally Good: Maximizing Monotone Submodular Functions over Sequences.
    CORR abs/2104.09884, 2021. [PDF]

  2. Yibo Zhang*, Chao Qian, and Ke Tang. Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization.
    CORR abs/1810.06833, 2018. [PDF]

Native Paper

  1. 钱超. 基于演化学习的子集选择研究进展. 中国人工智能学会通讯, 2020, 10(5): 15-21. [PDF]

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

  3. 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]

  4. 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]

  5. 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]

  6. 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]

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