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].
How Tight Can PAC-Bayes be in the Small Data Regime?
Authors: Andrew Foong, Wessel Bruinsma, David Burt, Richard Turner
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We study synthetic one-dimensional classification tasks in which it is feasible to meta-learn both the prior and the form of the bound to numerically optimise for the tightest bounds possible. |
| Researcher Affiliation | Collaboration | Andrew Y. K. Foong University of Cambridge EMAIL Wessel P. Bruinsma University of Cambridge Invenia Labs EMAIL David R. Burt University of Cambridge EMAIL Richard E. Turner University of Cambridge EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code to reproduce all experiments can be found at https://github.com/cambridge-mlg/pac-bayes-tightness-small-data. |
| Open Datasets | No | We choose especially simple learning tasks synthetic 1-dimensional binary classification problems, generated by thresholding Gaussian process (GP) samples. |
| Dataset Splits | Yes | Test set bounds rely on a subset of data which is not used to select the hypothesis, called a test set or held-out set. Let S = Strain Stest, with |S| = N, |Strain| = Ntrain and |Stest| = Ntest. [...] To compare PAC-Bayes DDPs against test set bounds, we sweep the prior/train set proportion from 0 to 0.8 and see what the tightest value obtained is. |
| Hardware Specification | No | The paper does not provide specific hardware details (like GPU/CPU models or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers, such as libraries or frameworks used in the experiments. |
| Experiment Setup | Yes | Hyperparameter details are given in Appendix I.7. |