Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Target alignment in truncated kernel ridge regression
Authors: Arash Amini, Richard Baumgartner, Dai Feng
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We provide experiments verifying the multiple-descent and non-monotonic behavior of the regularization curves as well as the improved rate of Theorem 2 (Section 4.2). We present various simulation results to demonstrate the multiple-descent and phase transition behavior of the regularization curves, and corroborate the theoretical results. |
| Researcher Affiliation | Collaboration | Arash A. Amini1, Richard Baumgartner2, Dai Feng3 1University of California, Los Angeles 2Merck & Co., Inc., Rahway, New Jersey, USA 3Data and Statistical Sciences, Abb Vie Inc. |
| Pseudocode | No | The paper contains mathematical derivations and proofs, but no structured pseudocode or algorithm blocks were found. |
| Open Source Code | Yes | The code for reproducing the simulations is available at [3]. |
| Open Datasets | No | The paper describes generating synthetic data for simulations ("200 samples generated from a uniform distribution on [0, 1]d") rather than using a publicly available dataset with specific access information. |
| Dataset Splits | No | The paper describes generating synthetic data for simulations but does not specify any explicit training, validation, or test dataset splits. |
| Hardware Specification | No | Our simulation were done on a regular laptop. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., programming languages, libraries, or frameworks) were mentioned in the paper. |
| Experiment Setup | No | The paper describes parameters for its simulations such as "Gaussian kernel e x x /2h2 in d = 4 dimensions with bandwidth h = p d/2" and fixing Ξ» for regularization curves. It also notes how random entries for ΞΎ are generated. However, it does not explicitly list hyperparameters or system-level training settings in the typical sense for a machine learning model. |