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When game theory meets machine learning
Date:2017/7/13     Browse:93
Speaker:   Prof. Tao Qin
Time:        July 13,1:00 pm-2:30 pm.

Location:  Room 1A-200  , SIST Building

Inviter:   Prof. Dengji Zhao

Abstract:

Machine learning aims at learning from data, while game theory studies conflict and cooperation between intelligent rational decision-makers. In recent years, learning systems have been formulated as games and conventional single-agent learning has been extended to multi-agent learning; game theory has been powered by machine learning techniques and games have been better analyzed and designed by leveraging data. In this talk, I will briefly introduce our recent work about the interaction between game theory and machine learning, including dual learning, generative adversarial networks, etc.

Bio:

秦涛博士,微软亚洲研究院主管研究员,中国科学技术大学兼职博导,于清华大学电子工程系获得学士和博士学位。他的主要研究领域包括机器学习和人工智能(重点是深度学习和强化学习的算法设计、理论分析及在实际问题中的应用),互联网搜索与计算广告,博弈论和多智能体系统。他在国际顶级会议和期刊上发表学术论文100余篇,曾任国际信息检索大会(SIGIR)、亚洲机器学习大会(ACML)、国际多智能体系统大会(AAMAS)领域主席,担任多个国际学术大会(NIPS、ICML、KDD、IJCAI、AAAI、WSDM、EC、SIGIR、AAMAS、WINE)程序委员会成员,曾任多个国际学术研讨会联合主席。


                                                                                                                  SIST-Seminar 17037