报 告 人：李宇峰 副教授，南京大学

报告时间：12月8日（周日）10:20～11:10

报告地点：乐乎新楼2号楼二层，学海厅

邀 请 人：岳晓冬 副教授

报告摘要：

Weakly supervised data is widely existed in real-world scenarios. Unlike supervised learning, which has achieved relatively mature solutions, weakly supervised learning is far from mature compared to supervised learning. Although various weakly supervised learning techniques have been applied in the industry, they lack clear guidelines to model the data due to the quality of raw data, so as to require lots of human intelligence involved into the modeling processes, which turn out to be not so reliable and smart. This talk first tries to figure out a couple of conditions that would be useful for deriving reliable weakly supervised learning. We then point out some possible conditions or resources that may be useful to derive reliable weakly supervised learning in future; finally we highlight some challenges remained in several complicated yet realistic weakly supervised learning scenarios.

报告人简介:

李宇峰，分别于2006年和2013年在南京大学计算机科学系获学士和博士学位。2013年进入南京大学计算机科学与技术系任助理研究员，现任软件新技术国家重点实验室副教授。他是LAMDA组的成员。他的研究兴趣主要是机器学习。特别是，他对弱监督学习、统计学习和优化感兴趣。他在顶级期刊和会议上发表了40多篇论文，如JMLR、TPAMI、AIJ、ICML、NIPS、AAAI等。他曾是顶级人工智能会议的高级程序委员，如IJCAI'15/17/19、AAAI'19/20等，以及《Neural Network》编委。曾担任IEEE Bigcomp2020共同程序主席、ACML2019 Tutorial共同主席等。曾获中国计算机学会优秀博士论文奖、江苏省优秀博士论文奖等。