Addressing Emerging Issues for Trustworthy Semiparametric/Machine Learning

The presentations in this session explore aspects that are important in linking real-world practices and the recent developments from nonparametric or semi parametric based machine learning research. These include enhancing privacy, interpretability, and considering practically feasible and meaningful evaluation criteria. In particular, under these constraints, we aim for topics that explore new grounds and methods that aim for high efficiency and correct inference outcomes.