Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness

Authors: Fredrik Hellström, Giuseppe Durisi

NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details

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 frehells@chalmers.se Giuseppe Durisi Chalmers University of Technology Gothenburg, Sweden durisi@chalmers.se
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]'.