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].
Fantastic Generalization Measures are Nowhere to be Found
Authors: Michael Gastpar, Ido Nachum, Jonathan Shafer, Thomas Weinberger
ICLR 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove mathematically that no such bound can be uniformly tight in the overparameterized setting; (2) bounds that may in addition also depend on the learning algorithm (e.g., stability bounds). For these bounds, we show a trade-off between the algorithm s performance and the bound s tightness. |
| Researcher Affiliation | Academia | Michael Gastpar EPFL EMAIL Ido Nachum University of Haifa EMAIL Jonathan Shafer MIT EMAIL Thomas Weinberger EPFL EMAIL |
| Pseudocode | No | The paper contains no pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper mentions 'MNIST' and 'CIFAR' as examples of natural image datasets but does not use them for experiments or provide access information. |
| Dataset Splits | No | The paper does not describe any experiments and therefore does not specify dataset splits for validation. |
| Hardware Specification | No | The paper does not describe the hardware used for any experiments as it is a theoretical work. |
| Software Dependencies | No | The paper does not list specific software components with version numbers as it is a theoretical work. |
| Experiment Setup | No | The paper does not describe any experimental setup, hyperparameters, or training settings as it is a theoretical work. |