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Curse of dimensionality refers to sparse phenomena of high-dimensional data, and it presents substantial challenges in the theory and computation of nonparametric models. This talk will present a class of regularization operators which enable sparse and smooth estimation of multi-dimensional functions in reproducing kernel Hilbert spaces. The operator leads to a unified framework for model selection to enhance the accuracy and interpretability of a variety of nonparametric models, including generalized additive models, partially linear models, and functional additive models. We discuss the theoretical properties of the estimator and demonstrate its empirical performance in real-world examples.

Dr. Zhang is available for individual or group meetings on Zoom after the talk until 16:00. Contact the seminar organizer for scheduling.