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.
Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness
Authors: Fredrik Hellström, Giuseppe Durisi
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,740 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,903 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 | If you are including theoretical results... (a) Did you state the full set of assumptions of all theoretical results? [Yes] We state the assumptions required for our results in Section 2 and the statements of the results. (b) Did you include complete proofs of all theoretical results? [Yes] Full proofs are included in the supplementary material. 3. If you ran experiments... [N/A] |
| Researcher Affiliation | Academia | Fredrik Hellström Chalmers University of Technology Gothenburg, Sweden EMAIL Giuseppe Durisi Chalmers University of Technology Gothenburg, Sweden EMAIL |
| Pseudocode | No | The paper does not include any figures, blocks, or sections labeled 'Pseudocode' or 'Algorithm', nor does it present structured steps for a method formatted like code. |
| Open Source Code | No | If you are including theoretical results... 3. If you ran experiments... (a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [N/A] |
| Open Datasets | No | The paper is theoretical and does not conduct empirical studies that would involve training on specific datasets. The section 'If you ran experiments...' explicitly states '[N/A]'. |
| Dataset Splits | No | The paper is theoretical and does not conduct empirical studies that would involve dataset splits for validation. The section 'If you ran experiments...' explicitly states '[N/A]'. |
| Hardware Specification | No | The paper is theoretical and does not describe experiments run on specific hardware. The section 'If you ran experiments...' explicitly states '[N/A]'. |
| Software Dependencies | No | The paper is theoretical and does not describe experiments requiring specific software dependencies with version numbers. The section 'If you ran experiments...' explicitly states '[N/A]'. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setups with hyperparameters or training settings. The section 'If you ran experiments...' explicitly states '[N/A]'. |