Concentration Inequalities for Conditional Value at Risk
Authors: Philip Thomas, Erik Learned-Miller
ICML 2019 | Conference PDF | Archive PDF | Plain Text | 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. |