Logistic Regression for Massive Data with Rare Events
Authors: Haiying Wang
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 <haiying.wang@uconn.edu>. |
| 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. |