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..
Concentration Inequalities for Conditional Value at Risk
Authors: Philip Thomas, Erik Learned-Miller
ICML 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In order to better visualize the benefits of our new inequalities relative to those of Brown (2007), we conducted a series of empirical comparisons. The results of these comparisons are presented in Figure 8. |
| Researcher Affiliation | Academia | 1College of Information and Computer Sciences, University of Massachusetts Amherst. |
| Pseudocode | No | The paper does not contain any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statements about releasing code or links to a code repository. |
| Open Datasets | No | The paper describes using generated samples from various distributions (log-normal, beta) for empirical comparisons, but it does not refer to a specific publicly available dataset with concrete access information (e.g., a link or formal citation). |
| Dataset Splits | No | The paper discusses empirical comparisons of inequalities using generated samples (e.g., "n = 10,000 samples"), but it does not specify train/validation/test dataset splits typically used for machine learning model training and evaluation. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory). |
| Software Dependencies | No | The paper does not list any specific software components or libraries with their version numbers. |
| Experiment Setup | Yes | In all cases, unless otherwise specified, we always used n = 10,000 samples, α = 0.05, and δ = 0.05. The sixth and seventh rows of Figure 8 show how the upper and lower bounds change as the amount of data, n, is varied. |