论文标题
集合内核方法，隐式正则化和确定点过程
Ensemble Kernel Methods, Implicit Regularization and Determinantal Point Processes
论文作者
论文摘要
通过使用确定点过程（DPP）的框架，可以获得有关多样性和正则化之间相互作用的一些理论结果。在本文中，我们表明，用KDPP进行抽样子集在无骑线核回归的背景下导致隐式正则化。此外，我们利用最先进的DPP算法的常见设置来采样多个小型子集，并将其用于无骑行回归的集合。我们的第一个经验结果表明，无骑线回归器的集合可能有趣地用于数据集，包括冗余信息。
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.
