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..
Connecting Optimization and Regularization Paths
Authors: Arun Suggala, Adarsh Prasad, Pradeep K. Ravikumar
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we conduct simulations to corroborate our theoretical findings. We use linear regression to empirically verify our results on connecting ridge-regression and gradient descent. We also corroborate our findings on excess risk and optimality of early-stopping rule for gradient descent. |
| Researcher Affiliation | Academia | Arun Sai Suggala Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Adarsh Prasad Carnegie Mellon University Pittsburgh, PA 15213 EMAIL Pradeep Ravikumar Carnegie Mellon University Pittsburgh, PA 15213 EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | No | The paper describes simulating data rather than using a publicly available or open dataset with access information. 'We simulate a linear model by drawing the covariates from an isotropic gaussian X N(0, Ip p) and the response y|x N( T x, σ2)' and 'We construct a classification dataset by drawing covariates X from isotropic gaussian i.e. X N(0, Ip).' |
| Dataset Splits | No | The paper describes how the data was simulated but does not provide specific dataset split information for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers. |
| Experiment Setup | Yes | We generate a sequence of iterates by GD with step size 0.01... We fix p = 100 and vary the samples n from 100 to 1500... We run GD with a step size = 0.123 and construct corresponding points on the regularization path ( (t) = t )... We fix the dimension p = 128 and the number of samples to n = 32. |