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..
Learning Juntas under Markov Random Fields
Authors: Gautam Chandrasekaran, Adam Klivans
NeurIPS 2025 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our paper is theoretical, there are no experiments. |
| Researcher Affiliation | Academia | Gautam Chandrasekaran EMAIL UT Austin Adam R. Klivans EMAIL UT Austin |
| Pseudocode | Yes | Algorithm 1: Find Relevant Variables(S, G, τ) ... Algorithm 2: Learn Junta(S, G, τ) |
| Open Source Code | No | Our paper is theoretical. The paper has no experiments and hence no code. |
| Open Datasets | No | Our paper is theoretical, there are no experiments. |
| Dataset Splits | No | Our paper is theoretical. We have no experiments. |
| Hardware Specification | No | Paper is theoretical. No experiments. |
| Software Dependencies | No | Our paper is theoretical. We have no experiments. |
| Experiment Setup | No | Our paper is theoretical. We have no experiments. |