Adaptively Partitioning Max-Affine Estimators for Convex Regression

28. Březen 2022

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O prezentaci

This paper considers convex shape-restricted nonparametric regression over subgaussian domain and noise with the squared loss.It introduces a tractable convex piecewise-linear estimator which precomputes a partition of the training data by an adaptive version of farthest-point clustering, approximately fits hyperplanes over the partition cells by minimizing the regularized empirical risk, and projects the result into the max-affine class.The analysis provides an upper bound on the generalization error of this estimator matching the rate of Lipschitz nonparametric regression and proves its adaptivity to the intrinsic dimension of the data mitigating the effect of the curse of dimensionality.The experiments conclude with competitive performance, improved overfitting robustness, and significant computational savings compared to existing convex regression methods.

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O organizátorovi (AISTATS 2022)

AISTATS is an interdisciplinary gathering of researchers at the intersection of computer science, artificial intelligence, machine learning, statistics, and related areas. Since its inception in 1985, the primary goal of AISTATS has been to broaden research in these fields by promoting the exchange of ideas among them. We encourage the submission of all papers which are in keeping with this objective at AISTATS.

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