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

I am an associate researcher at the School of Computer Science and Technology, University of Science and Technology of China (USTC). I received my B.Sc. and Ph.D. degrees in computer science from Nanjing University in 2009 and 2015, respectively, under the supervision of Prof. Zhi-Hua Zhou. I was a member of LAMDA Group, and visited the University of the Basque Country (Spain) and the University of Birmingham (UK), respectively.

Research Interest

My research interests include artificial intelligence, evolutionary algorithms and machine learning. 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 Mojgan Pourhassan and Frank Neumann) will organize a special session "Theory of Bio-Inspired Computation" at CEC 2019
• I (together with Yang Yu) gave a tutorial "Pareto Optimization for Subset Selection: Theory and Applications in Machine Learning" at WCCI 2018 [Slides]
• 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. Yibo Zhang*, Chao Qian, and Ke Tang. Maximizing Monotone DR-submodular Continuous Functions by Derivative-free Optimization.
   CORR abs/1810.06833, 2018. [PDF]
   
   
2. Chao Qian, Chao Bian, Yang Yu, Ke Tang, and Xin Yao. Analysis of Noisy Evolutionary Optimization When Sampling Fails.
   CORR abs/1810.05045, 2018. [PDF] (Extended from GECCO'18)
   
   
3. 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]
   
   
4. 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]
   

Journal Article

1. 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, in press. [Preprint PDF][PDF]
   
   
2. 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)
   
   
3. 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]
   
   
4. Chao Qian, Yang Yu, and Zhi-Hua Zhou. Analyzing Evolutionary Optimization in Noisy Environments.
   Evolutionary Computation, 2018, 26(1): 1-41. [Preprint PDF] [PDF]
   
   
5. 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]
   
   
6. 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]
   
   
7. 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 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, to appear.
   
   
2. 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]
   
   
3. 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]
   
   
4. 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]
   
   
5. 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)
   
   
6. 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)
   
   
7. 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]
   
   
8. 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]
   
   
9. 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]
   
   
10. 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)
    
    
11. 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)
    
    
12. 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]
    
    
13. 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)
    
    
14. 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)
    
    
15. 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]
    
    
16. 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]
    
    
17. 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)
    
    
18. 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]
    
    
19. 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)
    
    
20. 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]
    
    
21. 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)
    
    
22. 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]
    
    
23. 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]
    
    
24. 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)
    
    
25. 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]
    
    
26. 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]
    
    
27. 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)
    
    
28. 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)
    
    
29. 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

• Outstanding Doctoral Dissertation Award of CAAI (中国人工智能学会优博), 2017
• Outstanding Doctoral Dissertation Award of Jiangsu Province (江苏省优博), 2017
• Outstanding Doctoral Dissertation Award of Nanjing University (南京大学优博), 2017
• Young Elite Scientists Sponsorship Program by CAST (中国科协青年人才托举工程), 2016
• Outstanding Doctoral Dissertation Award of Jiangsu Computer Society (江苏省计算机学会优博), 2016
• Excellence Award of CCF-Tencent Open Research Fund, 2016
• IDEAL 2016 Best Paper Award (with Yang Yu and Zhi-Hua Zhou)
• IBM PhD Fellowship Award, 2014
• PAKDD 2012 Data Mining Competition Grand Prize Winner (with Nan Li, Shao-Yuan Li, Yue Zhu, Qing Da and Zhi-Hua Zhou)
• ACM GECCO 2011 Best Theory Paper Award (with Yang Yu and Zhi-Hua Zhou)

Students

Master Students

• 2016 - : Chao Bian 卞超 (获国家奖学金，CCF-A类论文1篇，CCF-B类论文2篇)
• 2017 - : Chao Feng 冯超 (CCF-A类论文2篇), Hang Xiong 熊航
• 2018 - : Feiyu Liu 刘飞宇

Undergraduate Students

• 2015 - : Yibo Zhang 张益博 (获郭沫若奖学金，CCF-A类论文1篇), Huijie Liu 刘慧杰
• 2014 - 2018: Tianhao Wang 王天浩 （Now: PhD candidate at Yale University）, Zhongjun Zhang 张仲骏 (Now: Alibaba)
• 2012 - 2016: Zhendong Lei 雷震东 (Now: PhD candidate at Chinese Academy of Sciences)

Professional Services

• Journal Reviewer:

Algorithmica
ACM Transactions on Intelligent Systems and Technology
Frontiers of Computer Science
IEEE Computational Intelligence Magazine
IEEE Transactions on Affective Computing
IEEE Transactions on Cognitive and Developmental Systems
IEEE Transactions on Cybernetics
IEEE Transactions on Evolutionary Computation
IEEE Transactions on Emerging Topics in Computational Intelligence
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Network Science and Engineering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Information Sciences
Journal of Machine Learning Research
Knowledge-Based Systems
Machine Learning
Memetic Computing
Neurocomputing
Pattern Recognition Letters
Swarm and Evolutionary Computation
SCIENCE CHINA Information Sciences
Theoretical Computer Science
中国科学
软件学报
计算机学报
自动化学报

• Conference PC Member:

  2019: AAAI, AISTATS, FOGA, ICML, PAKDD
  2018: ACML, AAAI, AISTATS, CEC, GECCO (Theory Track), ICML, IJCAI, IJCNN, LOD, NIPS (reviewer), PAKDD, PRICAI
  2017: ACML, AAAI, AISTATS, CEC, GECCO (Theory Track), IJCAI (senior PC), IJCNN, MOD, NIPS (reviewer), SDM, SEAL
  2016: ACML, CEC, GECCO (Theory Track), ICPR, IJCAI, MOD, NIPS (reviewer), PRICAI
  2015: IJCAI