Automatic Bayesian Density Analysis
Authors: Antonio Vergari, Alejandro Molina, Robert Peharz, Zoubin Ghahramani, Kristian Kersting, Isabel Valera5207-5215
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our extensive experimental evaluation, we demonstrate that ABDA effectively assists domain experts in both transductive and inductive settings. We empirically evaluate ABDA on synthetic and real-world datasets both as a density estimator and as a tool to perform several exploratory data analysis tasks. |
| Researcher Affiliation | Collaboration | Antonio Vergari antonio.vergari@tue.mpg.de MPI-IS, Tuebingen, Germany Alejandro Molina molina@cs.tu-darmstadt.de TU Darmstadt, Germany Robert Peharz rp587@cam.ac.uk University of Cambridge, UK Zoubin Ghahramani zoubin@cam.ac.uk University of Cambridge, UK Uber AI Labs, USA Kristian Kersting kersting@cs.tu-darmstadt.de TU Darmstadt, Germany Isabel Valera isabel.valera@tue.mpg.de MPI-IS, Tuebingen, Germany |
| Pseudocode | Yes | Algorithm 1 Gibbs sampling inference in ABDA |
| Open Source Code | Yes | Supplementary material and a reference implementation of ABDA are available at github.com/probabilistic-learning/ abda. |
| Open Datasets | Yes | From ISLV and MSPN original works we select 12 real-world datasets differing w.r.t. size and feature heterogeneity. Appendix C reports detailed dataset information... For example, the "Wine quality dataset" and "Abalone dataset" are commonly used public benchmarks. |
| Dataset Splits | Yes | For the transductive setting, we randomly remove either 10% or 50% of the data entries, reserving an additional 2% as a validation set for hyperparameter tuning (when required), and repeating five times this process for robust evaluation. For the inductive scenario, we split the data into train, validation, and test (70%, 10%, and 20% splits). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory) used for running its experiments. It lacks any mention of hardware specifications. |
| Software Dependencies | No | The paper states: "We implemented ABDA by leveraging the SPFlow library". While a library is mentioned, no specific version number for SPFlow or any other software dependency is provided. |
| Experiment Setup | Yes | In all experiments, we use a symmetric Dirichlet prior with γ = 10 for sum weights Ω and a sparse symmetric prior with α = 0.1 for the leaf likelihood weights wd j . For ABDA and ISLV, we run 5000 iterations of Gibbs sampling, discarding the first 4000 for burn-in. We learn MSPNs with the same hyper-parameters as for ABDA structure learning, i.e., stopping to grow the network when the data to be split is less than 10% of the dataset, while employing a grid search in {0.3, 0.5, 0.7} for the RDC dependency test threshold. |