2020/06/24 by Joachim Schreurs, Schreurs, Joachim, Michaël Fanuel +3
Computer Science · Engineering · Mathematics · Medicine · #3D Shape Modeling and Analysis #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Optical Imaging and Spectroscopy Techniques #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.2006.13701
openalex publication_date 2020/06/24 · arxiv created 2020/07/07 · arxiv updated 2021/07/22 · openalex created_date 2024/04/10 · openalex updated_date 2026/07/28
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.