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 [1].
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. |