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Page Revision: 2017/07/22 22:09

(中文简历) Yang Yu (Y. Yu) Can be pronounced as "young you" Ph.D., Associate Professor LAMDA Group Department of Computer Science National Key Laboratory for Novel Software Technology Nanjing University Office: 311, Computer Science Building, Xianlin Campus email: yuy@nju.edu.cn, eyounx@gmail.com

I received my Ph.D. degree in Computer Science from Nanjing University in 2011 (supervisor Prof. Zhi-Hua Zhou), and then joined the LAMDA Group (LAMDA Publications), Department of Computer Science and Technology of Nanjing University as an assistant researcher from 2011, and as an associate professor from 2014.

I am interested in artificial intelligence, particularly, applying theoretical-grounded evolutionary optimization to solve machine learning problems. Our research work has been published in international journals and conferences including Artificial Intelligence, IJCAI, KDD, etc., 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 National Outstanding Doctoral Dissertation Award in 2013 and China Computer Federation Outstanding Doctoral Dissertation Award in 2011.

(Detailed CV | CV in PDF)

Recent Update

• Welcome to submit your (under review) work to The 2nd Asian Workshop on Reinforcement Learning
• ZOOpt release 0.1 : a python package for derivative-free optimization (source code)
• Tutorials on Reinforcement Learning
• CIS Webminar: Towards Evolutionary Approximate Optimization for Machine Learning
• Invited Talk: On Evolutionary Machine Learning (PDF, 5.75MB) (The 12th China Workshop on Machine Learning and Applications, Nov. 2014, Xi'an, China)

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 evolutionary machine learning that applies theoretical-grounded evolutionary optimization to solve complex and non-convex problems for machine learning and reinforcement learning.

Full Publication List >>>

Selected work:

• High-dimensional derivative-free optimization (with Hong Qian and Yi-Qi Hu)
  Derivative-free optimization methods can tackle complex optimizations, however, are hard to scale to high dimensional problems, which is a bottleneck for wide applications of derivative-free optimization methods.

• Pareto optimization (with Chao Qian and Zhi-Hua Zhou)
  Pareto optimization is born from evolutionary algorithms. It has been shown to be a powerful approximate solver for constrained optimization problems in finite discrete domains, particularly the subset selection problem. We apply Pareto optimization for learning tasks including sparse regression, achieving extraordinary performance.

• Model-based derivative-free methods for optimization (with Hong Qian and Yi-Qi Hu)
  Derivative-free methods can tackle complex optimizations in real domains, such as non-convex, non-differentiable, and non-continuous problems with many local optima.

• Analysis of approximation ability of evolutionary optimization (with Chao Qian, Xin Yao and Zhi-Hua Zhou)
  Evolutionary algorithms are most commonly used to obtain "good-enough" solutions in practice, which relates to their approximation ability.

• General approaches to running time analysis of evolutionary optimization (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 for understanding these algorithms. We develop tools for analyzing the complexity of evolutionary algorithms.

• Functional representation for nonlinear reinforcement learning (with Peng-Fei Hou, Yu Qian, Qing Da and Zhi-Hua Zhou)
  Reinforcement learning seeks for a policy that receives the highest total reward from its environement. Functional representation is a powerful tool to approximate complex functions, which can help learn complex policies for fitting practical environments.

• 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. We show that diversity can play the role of regularization.

Codes

• High-dimensional derivative-free optimization

• Derivative-free optimization by classification

• Nonlinear reinforcement learning with functional representation

(My Goolge Scholar Citations)

Teaching

• Advanced Machine Learning. (for graduate students. Fall, 2017)
• Artificial Intelligence. (for undergraduate students. Spring, 2015, 2016, 2017)
• Data Mining. (for M.Sc. students. Fall, 2014, 2013, 2012)
• Digital Image Processing. (for undergraduate students from Dept. Math., Spring, 2014, 2013)
• Introduction to Data Mining. (for undergraduate students. Spring, 2013, 2012)

Students

• Recuit                                        >>>

• Students >>>

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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