南京大学计算机科学与技术系
软件新技术与产业化协同创新中心
摘 要：
Machine learning/deep learning has
achieved tremendous success in various application areas. Unfortunately, it
relies on huge high-quality labelled data and shows serious vulnerability to
adversarial examples. These issues dramatically hinder the deployment of
machine learning in practice, since most real-world data are easily imperfect
and corrupted. Therefore, in this talk, I will introduce our recent works on
trustworthy machine learning from a theoretical view of robust optimization,
including the reliability on noisy labels and the robustness against
adversarial examples.
报告人简介:
王奕森，北京大学信息科学技术学院，助理教授，博导。2018年博士毕业于清华大学计算机系，研究方向为机器学习，深度学习，在人工智能/机器学习领域顶级会议和期刊发表论文30余篇，包括ICML、NeurIPS、ICLR、CVPR、ICCV、ECCV、AAAI、IJCAI等。曾获得百度奖学金（全球共10位）、ACM中国优秀博士论文提名（全国共5位）等荣誉。
时间：11月9日
10:30-11:30
地点：计算机科学技术楼230室
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