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• W3157050624 abstract "In statistical learning, identifying underlying structures of true target functions based on observed data plays a crucial role to facilitate subsequent modeling and analysis. Unlike most of those existing methods that focus on some specific settings under certain model assumptions, this paper proposes a general and novel framework for recovering true structures of target functions by using unstructured M-regression in a reproducing kernel Hilbert space (RKHS). The proposed framework is inspired by the fact that gradient functions can be employed as a valid tool to learn underlying structures, including sparse learning, interaction selection and model identification, and it is easy to implement by taking advantage of the nice properties of the RKHS. More importantly, it admits a wide range of loss functions, and thus includes many commonly used methods, such as mean regression, quantile regression, likelihood-based classification, and margin-based classification, which is also computationally efficient by solving convex optimization tasks. The asymptotic results of the proposed framework are established within a rich family of loss functions without any explicit model specifications. The superior performance of the proposed framework is also demonstrated by a variety of simulated examples and a real case study." @default.
• W3157050624 created "2021-05-10" @default.
• W3157050624 creator A5012771471 @default.
• W3157050624 creator A5043907454 @default.
• W3157050624 creator A5051771646 @default.
• W3157050624 date "2019-01-03" @default.
• W3157050624 modified "2023-09-27" @default.
• W3157050624 title "Structure learning via unstructured kernel-based M-regression" @default.
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• W3157050624 hasPublicationYear "2019" @default.
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