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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
Integral Probability Metrics PAC-Bayes Bounds
Authors: Ron Amit, Baruch Epstein, Shay Moran, Ron Meir
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 6,411 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,626 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The entire paper is filled with mathematical definitions, theorems, lemmas, and proofs. No sections like 'Experiments', 'Results', 'Datasets', or 'Evaluation' are present. The abstract indicates a focus on generalizing PAC-Bayes bounds to IPMs, which is a theoretical contribution. |
| Researcher Affiliation | Academia | Pierre Alquier ENSAE - CREST, France; Alexandre Chrétien ENSAE - CREST, France |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, as it is a theoretical paper without an implemented methodology. |
| Open Datasets | No | The paper is theoretical and does not mention the use of any datasets, public or otherwise, for training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not discuss dataset splits (e.g., train/validation/test). |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training configurations. |