Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Learning under Model Misspecification: Applications to Variational and Ensemble methods
Authors: Andres Masegosa
NeurIPS 2020 | Venue PDF | LLM Run Details | Input Tokens: 17,900 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,478 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments with Bayesian neural networks illustrate these findings. |
| Researcher Affiliation | Academia | Andrés R. Masegosa University of Almería EMAIL |
| Pseudocode | No | The paper describes algorithms and refers to appendices for details but does not include structured pseudocode or algorithm blocks in the main text. |
| Open Source Code | Yes | The code to reproduce the results is available in https://github.com/PGM-Lab/PAC2BAYES. |
| Open Datasets | Yes | We performed the empirical evaluation on two data sets, Fashion-MNIST [58] and CIFAR-10 [30] |
| Dataset Splits | No | The paper does not explicitly provide training/validation/test split percentages or counts in the main text. It mentions 'Full details in Appendix D' but those details are not provided within the main paper's text. |
| Hardware Specification | No | No specific hardware (e.g., GPU models, CPU types, memory amounts) used for running experiments is explicitly mentioned in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, TensorFlow x.x, PyTorch x.x) are provided. |
| Experiment Setup | No | The paper mentions 'Full details in Appendix D' for the experimental setup but does not provide specific hyperparameter values or training configurations in the main text. |