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..
Sharp uniform convergence bounds through empirical centralization
Authors: Cyrus Cousins, Matteo Riondato
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experimental evaluation shows that our bounds greatly outperform non-centralized bounds and are extremely practical even at small sample sizes. |
| Researcher Affiliation | Academia | Cyrus Cousins Department of Computer Science Brown University Providence, RI 02912 EMAIL Matteo Riondato Department of Computer Science Amherst College Amherst, MA 01002 EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | The code is included in the supplementary material. |
| Open Datasets | No | The paper describes generating samples: "We generated the samples πfor our experiments from random distributions over βπ." It does not provide a link, DOI, or citation to a publicly available dataset for access. |
| Dataset Splits | No | The paper mentions varying sample size 'm' but does not specify explicit train/validation/test splits, percentages, or a method for creating such splits to ensure reproducibility. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we used πΏ= 0.01 and π= 32 (we comment on this choice below). The sample size πvaried from 4 (the minimum possible, due to Lemma 1) to 107. |