On the number of variables to use in principal component regression
Authors: Ji Xu, Daniel J. Hsu
NeurIPS 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We give an average-case analysis of the out-of-sample prediction error as p, n, N ! 1 with p/N ! and n/N ! β, for some constants 2 [0, 1] and β 2 (0, 1). In this average-case setting, the prediction error exhibits a double descent shape as a function of p. We also establish conditions under which the minimum risk is achieved in the interpolating (p > n) regime. The proofs of the results are detailed in the full version of the paper [19]. |
| Researcher Affiliation | Academia | Ji Xu Columbia University jixu@cs.columbia.edu Daniel Hsu Columbia University djhsu@cs.columbia.edu |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about open-source code availability or links to repositories. |
| Open Datasets | No | The paper uses a synthetic data model ('Our data (x1, y1), . . . , (xn, yn) are assumed to be i.i.d. with xi N(0, )') for theoretical analysis and does not mention or provide access to any public datasets. |
| Dataset Splits | No | The paper is theoretical and analyzes a synthetic data model. It does not describe any specific training, validation, or test dataset splits for empirical reproduction. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper does not provide any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an empirical experimental setup with hyperparameters or system-level training settings. |