Exchangeability-Aware Sum-Product Networks
Authors: Stefan Lüdtke, Christian Bartelt, Heiner Stuckenschmidt
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We introduce a structure learning algorithm for XSPNs and empirically show that they can be more accurate than conventional SPNs when the data contains repeated, interchangeable parts. and 4 Experimental Evaluation |
| Researcher Affiliation | Academia | Stefan L udtke1 , Christian Bartelt1 and Heiner Stuckenschmidt2 1Institute for Enterprise Systems, University of Mannheim, Germany 2Data and Web Science Group, University of Mannheim, Germany |
| Pseudocode | Yes | Algorithm 1 Learn XSPN |
| Open Source Code | Yes | Our implementation2 of XSPNs is based on the SPFlow library [Molina et al., 2019] for Python. 2Available at https://github.com/stefanluedtke/XSPNFlow |
| Open Datasets | Yes | The senate dataset contains all 720 roll call votes in the Senate of the 116th United States Congress, taken from [Lewis et al., 2021]. and 17th German federal parliament (bundestag dataset, data taken from [Bergmann et al., 2018]). |
| Dataset Splits | Yes | We performed an exhaustive grid search of the SPN hyperparameters. and Table 1 shows the experimental results. For the SPN and XSPN, results that are significantly better than the other (X)SPN model are printed in bold (paired t-test, p < 0.05). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or specific cloud instance types) used for running the experiments. |
| Software Dependencies | No | Our implementation of XSPNs is based on the SPFlow library [Molina et al., 2019] for Python. The paper mentions software tools like SPFlow and Python but does not provide specific version numbers for them (e.g., Python 3.x, SPFlow vX.Y). |
| Experiment Setup | Yes | We performed an exhaustive grid search of the SPN hyperparameters. Specifically, we varied the g-test threshold values ρ {5, 15}, the minimum number of instances m {20, 200}, and the significance level of the χ2-test for exchangeability p {0.05, 0.1, 0.2, 0.4}. For all models, we used Laplace smoothing with α = 0.1. |