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Beside the minimizationof the prediction error, two of the most desirable properties of a regression scheme are stability and interpretability . Driven by these principles, we propose continuous-domain formulations for one-dimensional regression problems. In our first approach, we use the Lipschitz constant as a regularizer, which results in an implicit tuning of the overall robustness of the learned mapping.
September 26-28, 2022
Location: Shanghai, China
Submission Deadline: May 30, 2022
Call for Proposals Document
October 26-November 11, 2022
Location: Yokohama, Japan
CFP Announcement
Submission Deadline: April 10, 2022
Call for Proposals Document
Manuscript Due: November 30, 2022
Publication Date: September 2023
CFP Document