Structure learning via unstructured kernel-based M-regression
This provides a general method for structure recovery in statistical learning, applicable across various loss functions, but it appears incremental as it builds on existing gradient-based and kernel-based techniques.
The paper tackles the problem of identifying underlying structures of target functions in statistical learning by proposing a general framework using unstructured M-regression in a reproducing kernel Hilbert space, which is computationally efficient and demonstrated to perform well in simulations and a real case study.
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.