Virtual
Title: A Statistical Journey through Trustworthy AIAbstract: Our lab believes that the next generation of AI is mainly driven by trustworthiness, beyond performance. This talk attempts to offer statistical perspectives to embrace three challenges in trustworthy AI: privacy, robustness and fairness. Specifically, we consider privacy protection by machine un-learning, enhance adversarial robustness by utilizing artificially generated data, and establish fair Bayes-optimal classifiers. These results demonstrate the unique value of statisticians in studying trustworthy AI from empirical, methodological or theoretical aspects. Part of this talk are based on the following works: https://arxiv.org/pdf/2202.06996.pdf (https://arxiv.org/pdf/2202.06996.pdf), http://proceedings.mlr.press/v130/li21a/li21a.pdf (http://proceedings.mlr.press/v130/li21a/li21a.pdf)，https://arxiv.org/pdf/2202.09724.pdf (https://arxiv.org/pdf/2202.09724.pdf).