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Page Revision: 2014/04/11 22:49

Yang Yu (Y. Yu) Can be pronounced as "young you" Ph.D., Assistant Researcher LAMDA Group Department of Computer Science National Key Laboratory for Novel Software Technology Nanjing University Office: 919, Computer Science Building, Xianlin Campus email: yuy@lamda.nju.edu.cn, eyounx@gmail.com, yuy@nju.edu.cn (the last email is both symbolic and problematic)

I received my Ph.D. degree in Computer Science from Nanjing University in 2011 (supervisor Prof. Zhi-Hua Zhou), and then joined the Department of Computer Science and Technology of Nanjing University as an assistant researcher in 2011.

I am interested in artificial intelligence, particularly, evolutionary computation and machine learning. Our research work has been published in journals (e.g. Artificial Intelligence) and conferences (e.g. IJCAI), and granted several awards such as Grand Champion (with other LAMDA members) of PAKDD'06 Data Mining Competition, the best paper award of PAKDD'08, the best theory paper award of GECCO'11. My dissertation was granted honours including China Computer Federation Outstanding Doctoral Dissertation Award in 2011 and Jiangsu Province Outstanding Doctoral Dissertation Award in 2012.

(Detailed CV)

Research

I am interested in investigating towards the goal of artificial intelligence. As conceived by Alan Turing, one possible way of building an intelligent machine is to evolve learning machines, which now drops into multiple subfields of artificial intelligence, especially machine learning and evolutionary computation. Currently I am focusing on the subfields:

• Theoretical foundation of evolutionary algorithms (my related publications): to understand and reveal the power of evolutionary algorithms;
• Ensemble learning (my related publications): to achieve the strongest generalization ability from finite samples;
• Reinforcement learning (my related publications): to be intelligent to survive in environments autonomously.

Selected work: (full publication list)

(Evolutionary Optimization)

• Sampling-and-learning (SAL) framework: (with Hong Qian)
  Most of evolutionary algorithms share a common algorithm structure, which has been argued to involve the sampling and the model building stages. In (PDF-cec14), we developed the sampling-and-learning (SAL) framework to capture the essence of these algorithms, and analyzed its approximation performance under a probability. Focusing on the sampling-and-classification (SAC) algorithms, which specify the learning sub-routine of SAL as a binary classification, we dervied a general performance bound, with the help of the learning theory. We also disclosed conditions under which SAC algorithms can achieve polynomial and super-polynomial speedup to random search.

• Approximation ability of evolutionary optimization: (with Xin Yao and Zhi-Hua Zhou)
  Evolutionary algorithms are most commonly used to obtain good-enough solutions rather than optimal solutions in practice, which relates to their approximation ability. We developed the SEIP framework (PDF-aij12) to characterize the approximation ability of evolutionary algorithms, and showed that they can achieve the currently best-achievable approximation ratio for the k-set cover problem, which also reveals the advantage of evolutionary algorithms over the well known greedy algorithm.

• General approaches to running time analysis of metaheuristic optimization algorithms: (with Chao Qian and Zhi-Hua Zhou)
  The running time, or the computational complexity, of evolutionary algorithms and other metaheuristic algorithms is one of the most important theoretical issues to understand these algorithms.
  • We developed the convergence-based approach (PDF-aij08) that can be applied to obtain running time bounds of a large range of metaheuristic algorithms.
  • By comparing two algorithms, we developed a new analysis tool, switch analysis (tr11|PDF-ppsn10) that can lead to tighter running time bounds.
    • Using switch analysis to compare problem instances, we can identify the hardest and the easiest problem instances for a metaheuristic algorithm in a problem class (PDF-ppsn12).

(Machine Learning)

• Using functional representation in reinforcement learning: (with Yu Qian, Qing Da and Zhi-Hua Zhou)
  Reinforcement learning seeks the policy that receives the highest total reward from its environement. Real-world applications are often so complex that complex policies are appealing. Functional representation, known as boosting techniques widely used in supervised learning, is a powerful tool to approximate complex functions but has been little investigated in reinforcement learning.
  • By functional representation, a policy can be a sum of many basis functions, which causes a high time cost especially for reinforcement learning. We tackle this time cost barrier by the napping mechanism, which results a significant improvement in time as well as a potential improvement in total reward (PDF-aamas14 / code)

• The role of diversity in ensemble learning: (with Nan Li, Yu-Feng Li and Zhi-Hua Zhou)
  Ensemble learning is a machine learning paradigm that achieves the state-of-the-art performance. Diversity was believed to be a key to a good performance of an ensemble approach, which, however, previously served only as a heuristic idea.
  • By the diversity regularized machine (PDF-ijcai11 / code), we showed that diversity plays a role of regularization as in popular statistical learning approaches.
  • We proved that diversity defined on hypothesis output space plays a role of regularization, and proposed the diversity regularized ensemble pruning to prune Bagging classifiers. (PDF-ecml12 / code)

Codes

• Reinforcement learning with functional represented policy

(My Goolge Scholar Citations)

Recent Professional Activities

• Invited Talk: Analyzing Evolutionary Optimization in Noisy Environments (The 8th NICaiA Workshop on Nature Inspired Computation and Its Applications, Oct. 2013, Nanjing, China)
• Our tutorial "An Introduction on Evolutionary Optimization: Recent Theoretical and Practical Advances" (with Ke Tang, Xin Yao and Zhi-Hua Zhou) has been accepted by IJCAI'13 (IJCAI Schedule) (Tutorial Webpage)
• Invited Talk: Approximate Ability of Evolutionary Algorihtms (The Fourth NICaiA Workshop on Nature Inspired Computation and Its Applications, Oct. 2012, Xi'an, China)
• Invited Talk: Revisit of Switch Analysis (The Third NICaiA Workshop on Nature Inspired Computation and Its Applications, April 2012, Birmingham, U.K.)
• Invited Talk: An analysis on recombination in multi-objective evolutionary optimization (ICONIP'2011 Workshop on Recent Advances in Nature Inspired Computation and Its Applications, Nov. 2011, Hangzhou, China)
• Invited Talk: Analyzing sophisticated metaheuristic algorithms (The 2011 International Workshop on Nature Inspired Computation and Applications, April 2011, Hefei, China)

Teaching

• Digital Image Processing. (for undergraduate students from Dept. Math., Spring, 2014) >>>Course Page>>>
  
• Data Mining. (for M.Sc. students. Fall, 2013)
  
• Introduction to Data Mining. (for undergraduate students. Spring, 2013)
  
• Digital Image Processing. (for undergraduate students. Spring, 2013)
  
• Data Mining. (for M.Sc. students. Fall, 2012)
  
• Introduction to Data Mining. (for undergraduate students. Spring, 2012)
  
• (Assistant) Data mining. (for graduate students. Fall, 2007) Teaching Assistant Page
  
• (Assistant) Algorithm Design and Analysis. (for undergraduate students. Fall, 2005)

Mail:
National Key Laboratory for Novel Software Technology, Nanjing University, Xianlin Campus Mailbox 603, 163 Xianlin Avenue, Qixia District, Nanjing 210023, China
(In Chinese:) 南京市栖霞区仙林大道163号，南京大学仙林校区603信箱，软件新技术国家重点实验室，210023。

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