报告题目：Towards Trustworthy Learning and Reasoning under Noisy Data

报告时间：2023年5月8日9:00

报告地点：线上腾讯会议（#腾讯会议：831-191-110）

报告人：韩波

报告人国籍：中国

报告人单位：香港浸会大学

报告人简介：韩波，香港浸会大学计算机系助理教授，领导TMLR研究组。他同时兼任RIKEN人工智能项目梅峰访问科学家，并曾兼任Microsoft研究院访问科学家。他于2019-2020年博士后于RIKEN人工智能项目；2019年博士毕业于悉尼科技大学。他的研究广泛涉猎了机器学习的诸多理论和实践领域，目前关注重点包括可信赖表示学习，因果表示学习，基础模型等方法，以及这些方法在自然科学和交叉学科的应用。他的研究得到众多政府和业界资金支持，并获得众多奖项，包括三项政府研究奖 (RGC CAREER, NSFC Young Scientist, RIKEN BAIHO)，和五项企业研究奖 (Microsoft, NVIDIA, Huawei, Tencent, Alibaba)。

报告摘要：Trustworthy learning and reasoning are the emerging and critical topics in modern machine learning, since most real-world data are easily noisy, such as online transactions,healthcare,cyber-security, and robotics. Intuitively, trustworthy intelligent system should behave more human-like, which can learn and reason from noisy data. Therefore, in this talk, I will introduce trustworthy learning and reasoning from three human-inspired views, including reliability, robustness, and interaction. Specifically, reliability will consider uncertain cases, namely deep learning with noisy labels. Meanwhile, robustness will discuss adversarial conditions, namely deep learning with noisy (adversarial) features. Then, interaction will focus on the dynamic interaction between noisy labels and noisy features. Besides labels and features, I will discuss other noisy data, such as noisy domains, noisy demonstrations, and noisy graphs. Furthermore, I will introduce the newly established Trustworthy Machine Learning and Reasoning (TMLR) Group at Hong Kong SAR and Greater Bay Area.

邀请人：杜博