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..
Logistic Regression for Massive Data with Rare Events
Authors: Haiying Wang
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Section 5 presents some numerical experiments, and Section 6 concludes the paper and points out some necessary future research. ... We repeat the simulation for S = 1,000 times and calculate empirical MSEs |
| Researcher Affiliation | Academia | 1Department of Statistics. Correspondence to: Hai Ying Wang <EMAIL>. |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described. |
| Open Datasets | No | The paper generates synthetic data for its numerical experiments: 'The covariates xi s are generated from N(1, 1) for cases (yi = 1) and from N(0, 1) for controls (yi = 0).' It does not use a publicly available or open dataset with access information. |
| Dataset Splits | No | The paper does not provide specific dataset split information for training, validation, or testing. It describes generating full data and then under-sampling controls from it, but not fixed splits. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | No | The paper describes how the synthetic data for the numerical experiments was generated (e.g., P(y=1) values, covariate distributions, true parameter values), but it does not specify hyperparameters or system-level training settings for a learning algorithm itself, as the primary focus is on MLE properties rather than an iterative model training process. |