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..
Closed-form Estimators for High-dimensional Generalized Linear Models
Authors: Eunho Yang, Aurelie C. Lozano, Pradeep K. Ravikumar
NeurIPS 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We corroborate the surprising statistical and computational performance of our class of estimators via extensive simulations. |
| Researcher Affiliation | Collaboration | Eunho Yang IBM T.J. Watson Research Center EMAIL Aurelie C. Lozano IBM T.J. Watson Research Center EMAIL Pradeep Ravikumar University of Texas at Austin EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that open-source code for the methodology is available. |
| Open Datasets | No | The paper uses simulated data, which it describes how to generate, rather than a publicly available or open dataset for which access information is provided. |
| Dataset Splits | Yes | While our theorem specified an optimal setting of the regularization parameter λn and , this optimal setting depended on unknown model parameters. Thus, as is standard with high-dimensional regularized estimators, we set tuning parameters λn = c log p/n and = c0p log p/n by a holdoutvalidated fashion; finding a parameter that minimizes the 2 error on an independent validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | Yes | We compare against standard 1 regularized MLE estimators with iteration bounds of 50, 100, and 500, denoted by 1 MLE1, 1 MLE2 and 1 MLE3 respectively. Noting that our theoretical results were not sensitive to the setting of in M(y), we simply report the results when = 10 4 across all experiments. we set tuning parameters λn = c log p/n and = c0p log p/n by a holdoutvalidated fashion; finding a parameter that minimizes the 2 error on an independent validation set. |