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..
Message Passing for Collective Graphical Models
Authors: Tao Sun, Dan Sheldon, Akshat Kumar
ICML 2015 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate NLBP with two sets of experiments. First, we evaluate the extent to which NLBP accelerates CGM inference and learning for a benchmark synthetic bird migration problem (Sheldon et al., 2013; Liu et al., 2014). Then, we demonstrate the benefits of a more scalable inference algorithm by evaluating CGMs in a new application: learning with noisy sufficient statistics. |
| Researcher Affiliation | Academia | 1University of Massachusetts Amherst, 2Mount Holyoke College, 3Singapore Management University |
| Pseudocode | Yes | Algorithm 1: Non-Linear Belief Propagation |
| Open Source Code | No | The paper does not provide an explicit statement or link for open-source code related to the methodology described. |
| Open Datasets | No | Synthetic data is generated from a chain-structured CGM to simulate migration of a population of M birds from the bottomleft corner to the top-right corner of an ℓ ℓgrid. |
| Dataset Splits | No | The paper generates synthetic data and simulates trajectories rather than explicitly defining training, validation, and test splits from a pre-existing dataset. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'MATLAB's interior-point algorithm' but does not specify the version numbers for MATLAB or any other software dependencies. |
| Experiment Setup | Yes | In the following experiments, we set M =1000, T =20 and vary grid size L from 5 5 to 19 19. We report results for wtrue =(5, 10, 10, 10). we added Poisson noise y Pois(αn) to the nodes, with detection rate α=1. For the CGM-based algorithms, we ran 250 EM iterations, which was enough for convergence in almost all cases. |