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Abstract:
Estimating the full (conditional) distribution is crucial to many applications. However, existing methods such as quantile regression typically struggle with high-dimensional response variables. To this end, distributional learning models the target distribution via a generative model, which enables inference via sampling. In this talk, we introduce a new distributional learning method called engression. We then demonstrate the applications of engression to several statistical problems that either involve distribution estimation (e.g., distributional causal effect estimation, distributionally lossless dimension reduction) or require stronger identification criteria (e.g., extrapolation in nonparametric regression, causal effect identification), as well as scientific problems such as climate downscaling.
Bio:
Xinwei Shen is a postdoctoral researcher at ETH Zürich working with Peter Bühlmann and Nicolai Meinshausen and will be joining UW Statistics as an assistant professor. She obtained her PhD at HKUST advised by Tong Zhang and a Bachelor of Science degree at Fudan University. Her research interests include distributional learning, causality, robustness, and applications in climate science.
Part of the Siebel School Speakers Series. Faculty Host:
Meeting ID: 875 4269 3582
Passcode: csillinois
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