Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes
Machine Learning
2021-07-22 v3 Machine Learning
Abstract
By using the framework of Determinantal Point Processes (DPPs), some theoretical results concerning the interplay between diversity and regularization can be obtained. In this paper we show that sampling subsets with kDPPs results in implicit regularization in the context of ridgeless Kernel Regression. Furthermore, we leverage the common setup of state-of-the-art DPP algorithms to sample multiple small subsets and use them in an ensemble of ridgeless regressions. Our first empirical results indicate that ensemble of ridgeless regressors can be interesting to use for datasets including redundant information.
Cite
@article{arxiv.2006.13701,
title = {Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes},
author = {Joachim Schreurs and Michaël Fanuel and Johan A. K. Suykens},
journal= {arXiv preprint arXiv:2006.13701},
year = {2021}
}