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..
Convergence of Some Convex Message Passing Algorithms to a Fixed Point
Authors: Vaclav Voracek, Tomas Werner
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove a stronger result (conjectured before but never proved): the iterates converge to a fixed point of the method. Moreover, we show that the algorithm terminates within O(1/ε) iterations. We first prove this for a version of coordinate descent applied to a general piecewise-affine convex objective. |
| Researcher Affiliation | Academia | 1T ubingen AI center, University of T ubingen 2Dept. of Cybernetics, Faculty of Electrical Engineering, Czech Technical University in Prague. |
| Pseudocode | Yes | Algorithm 1 Minimizing pointwise maximum of affine functions by coordinate descent; Algorithm 2 Max-sum diffusion; Algorithm 3 Max-marginal averaging |
| Open Source Code | No | The paper does not include an unambiguous statement about releasing code for the methodology described, nor does it provide a direct link to a source-code repository. |
| Open Datasets | No | The paper is purely theoretical and does not use any datasets for training or evaluation. Example 3.1 describes a hypothetical problem, but no dataset is used. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments with data. Therefore, there is no mention of dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup involving specific hardware. |
| Software Dependencies | No | The paper is theoretical and does not describe any experimental setup involving specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameters or system-level training settings. |