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..
Concavity of reweighted Kikuchi approximation
Authors: Po-Ling Loh, Andre Wibisono
NeurIPS 2014 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conclude with simulations that demonstrate the advantages of the reweighted Kikuchi approach. |
| Researcher Affiliation | Academia | Po-Ling Loh Department of Statistics The Wharton School University of Pennsylvania EMAIL Andre Wibisono Computer Science Division University of California, Berkeley EMAIL |
| Pseudocode | No | Section 4 describes the algorithm using mathematical equations for message updates (16) and pseudomarginal computations (17), but these are not presented in a formal pseudocode block or algorithm structure. |
| Open Source Code | No | The paper does not provide any information about the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper generates random potential functions for its experiments on complete graphs and toroidal grid graphs; it does not use or provide access to a public dataset. |
| Dataset Splits | No | The paper conducts simulations with specific parameters but does not describe conventional train, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies or their version numbers, such as programming languages, libraries, or solvers. |
| Experiment Setup | Yes | We use a damping factor of λ = 0.5, convergence threshold of 10 10 for the average change of messages, and at most 2500 iterations. We repeat this process with at least 8 random initializations for each value of . |