Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Target alignment in truncated kernel ridge regression
Authors: Arash Amini, Richard Baumgartner, Dai Feng
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,315 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,263 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |