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..
Overparameterization Improves Robustness to Covariate Shift in High Dimensions
Authors: Nilesh Tripuraneni, Ben Adlam, Jeffrey Pennington
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Figure 1: The asymptotic predictions of Thm. 5.1 as a function of the overparameterization ratio (φ/ = n1/m) and the shift power ( ) for the (2, )-diatomic LJSD (Eq. (9)) with φ = n0/m = 0.5, σ = Re LU, γ = 0.001, and σ2 = 0.1. ... Markers in (d,e,f) show simulations for n0 = 512 and agree well with the asymptotic predictions. |
| Researcher Affiliation | Collaboration | Nilesh Tripuraneni U.C. Berkeley EMAIL Ben Adlam Brain Team, Google Research EMAIL Jeffrey Pennington Brain Team, Google Research EMAIL |
| Pseudocode | No | The paper presents mathematical formulas and theoretical derivations but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the release of open-source code for the described methodology. |
| Open Datasets | Yes | We consider the task of learning an unknown function from m i.i.d. samples (xi, yi) 2 Rn0 R for i 2 {1, . . . , m}, where the covariates are Gaussian, xi N(0, ) with positive definite covariance matrix , and the labels are generated by a linear function parameterized by β 2 Rn0, drawn from N (0, In0). |
| Dataset Splits | No | The paper describes the training and test distributions and their characteristics but does not explicitly mention or specify any validation dataset splits. |
| Hardware Specification | No | The paper mentions running simulations (e.g., "simulations for n0 = 512" in Figure 1), but it does not provide any specific details about the hardware used, such as GPU/CPU models, memory, or cloud resources. |
| Software Dependencies | No | The paper does not provide specific software dependencies, such as programming languages or libraries with their version numbers, that would be needed to replicate the experiments. |
| Experiment Setup | Yes | Figure 1: The asymptotic predictions of Thm. 5.1 as a function of the overparameterization ratio (φ/ = n1/m) and the shift power ( ) for the (2, )-diatomic LJSD (Eq. (9)) with φ = n0/m = 0.5, σ = Re LU, γ = 0.001, and σ2 = 0.1. ... Numerical predictions from Thm. 5.1 can be obtained by first solving the self-consistent equation for x by fixed-point iteration, x 7! 1 γ !+I1,1 , and then plugging the result into the remaining terms. |