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].
Integral Probability Metrics PAC-Bayes Bounds
Authors: Ron Amit, Baruch Epstein, Shay Moran, Ron Meir
NeurIPS 2022 | Venue PDF | LLM Run Details
| 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. |