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。

地点：计算机科学技术楼230室