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..
Low Degree Hardness for Broadcasting on Trees
Authors: Han Huang, Elchanan Mossel
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we prove that this is indeed the case for low degree polynomials. We show that for the broadcast problem using any Markov chain on trees with N leaves, below the Kesten Stigum bound, any O(log N) degree polynomial has vanishing correlation with the root. Our result is one of the first low-degree lower bound that is proved in a setting that is not based or easily reduced to a product measure. |
| Researcher Affiliation | Academia | Han Huang Department of Mathematics University of Missouri Columbia, MO 65203 EMAIL Elchanan Mossel Department of Mathematics MIT Cambridge, MA 02139 EMAIL |
| Pseudocode | No | The paper is purely theoretical, focusing on mathematical definitions, theorems, and proofs. It does not include any pseudocode or algorithm blocks. |
| Open Source Code | No | The paper is a theoretical work and does not mention or provide access to any open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not describe or use any datasets, public or otherwise, for training or other experimental purposes. |
| Dataset Splits | No | The paper is theoretical and does not involve experimental data splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not involve experiments that would require specific hardware. No hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs, not software implementation. Therefore, no software dependencies with specific version numbers are mentioned. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setups, hyperparameters, or training configurations. |