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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Graph-based Discriminators: Sample Complexity and Expressiveness
Authors: Roi Livni, Yishay Mansour
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the expressiveness of families of k-ary functions, compared to the classical hypothesis class H, which is k = 1. We show a separation in expressiveness of k + 1-ary versus k-ary functions. This demonstrate the great beneο¬t of having k 2 as distinguishers. For k 2 we introduce a notion similar to the VC-dimension, and show that it controls the sample complexity. We proceed and provide upper and lower bounds as a function of our extended notion of VC-dimension. |
| Researcher Affiliation | Collaboration | Roi Livni Tel Aviv University EMAIL Yishay Mansour Tel Aviv University and Google EMAIL |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not mention providing open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not involve empirical experiments with datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with validation splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any software dependencies with specific version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |