Gaussian Ensemble Belief Propagation for Efficient Inference in High-Dimensional, Black-box Systems
Authors: Dan MacKinlay, Russell Tsuchida, Daniel Pagendam, Petra Kuhnert
ICLR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare GEn BP against an alternative belief propagation method, Ga BP, and, for reference, a global Laplace approximation (Mackay, 1992). We use synthetic benchmarks designed to assess performance in high-dimensional, nonlinear dynamical systems. In both cases, the graph structure is a randomized system identification task (Appendix A.1), where a static parameter influences a noisily observed, nonlinear dynamical system. |
| Researcher Affiliation | Academia | Dan Mac Kinlay CSIRO s Data61 Dan.Mac EMAIL Russell Tsuchida Monash University Dan Pagendam CSIRO s Data61 Petra Kuhnert CSIRO s Data61 |
| Pseudocode | Yes | Algorithm 1 Loopy Low-rank Belief Propagation over Factor Graph G Algorithm 2: GEn BP Algorithm 3: GEn BP fj xℓMessage (Single Incoming) |
| Open Source Code | Yes | Supporting code is available at github.com/danmackinlay/GEn BP. |
| Open Datasets | No | We use synthetic benchmarks designed to assess performance in high-dimensional, nonlinear dynamical systems. In both cases, the graph structure is a randomized system identification task (Appendix A.1), where a static parameter influences a noisily observed, nonlinear dynamical system. |
| Dataset Splits | No | The experiments described in the paper primarily use synthetic data generated by models like the 1D Transport Model and Navier Stokes System. While they mention running multiple simulations (e.g., "n = 10 runs", "n = 40 simulations", "n = 80 runs") for statistical robustness, and a temporal split for domain adaptation in Appendix B.3 ("GEn BP is applied to the first 5 time steps for domain adaptation"), there are no explicit training, validation, or test dataset splits in the conventional machine learning sense for pre-existing datasets. |
| Hardware Specification | Yes | Experiments were conducted on a Dell Power Edge C6525 Server with AMD EPYC 7543 32-Core Processors running at 2.8 GHz (3.7 GHz turbo) with 256 MB cache. Float precision is set to 64 bits, and memory usage is capped at 32 GB. |
| Software Dependencies | No | The paper mentions using "a PyTorch implementation from Li et al. (2020)" for the Navier Stokes equation. However, it does not specify the version number of PyTorch or any other software libraries, compilers, or operating systems used in the experiments. |
| Experiment Setup | Yes | Unless otherwise specified, the hyperparameters for both Ga BP and GEn BP are set to γ2 = 0.01 and σ2 = 0.001. GEn BP has additional hyperparameters η2 = 0.1 and ensemble size N = 64. We cap the number of message-propagation descent iterations at 150 and relinearise or re-simulate after every 10 steps. |