报告题目：Towards Trustworthy Machine Learning
报告时间：12月21日 14:00 ~ 15:00
报告地点：信息北楼A404 

报告摘要：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.

讲者简介：王奕森，北京大学助理教授，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。

王奕森老师个人主页：http://www.cis.pku.edu.cn/info/1084/1637.htm (中文)   https://sites.google.com/site/csyisenwang/ （英文）

下面是王老师2020年最新的论文，欢迎大家一起来讨论：

• Adversarial Weight Perturbation Helps Robust Generalization，Dongxian Wu, Shu-Tao Xia, Yisen Wang# , NeurIPS 2020.

• Normalized Loss Functions for Deep Learning with Noisy Labels, Xingjun Ma*, Hanxun Huang*, Yisen Wang#, Simone Romano, Sarah Erfani and James Bailey, ICML 2020.

• Improving Adversarial Robustness Requires Revisiting Misclassified Examples, Yisen Wang*, Difan Zou*, Jinfeng Yi, James Bailey, Xingjun Ma, Quanquan Gu.  ICLR 2020

• Skip Connections Matter: On the Transferability of Adversarial Examples Generated with ResNets . Dongxian Wu, Yisen Wang#, Shu-Tao Xia, James Bailey, Xingjun Ma. ICLR 2020

• Adversarial Camouflage: Hiding Physical-World Attacks with Natural Styles . Ranjie Duan, Xingjun Ma, Yisen Wang, James Bailey, Kai Qin, Yun Yang. CVPR 2020.

• Improving Query Efficiency of Black-box Adversarial Attack, Yang Bai*, Yuyuan Zeng*, Yong Jiang#, Yisen Wang#, Shu-Tao Xia, Weiwei Guo.  ECCV 2020.