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..
Testing Probabilistic Circuits
Authors: Yash Pralhad Pote, Kuldeep S Meel
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the practical efficiency of our algorithmic framework via a detailed experimental evaluation of a prototype implementation against a set of 475 PC benchmarks. |
| Researcher Affiliation | Academia | Yash Pote r Kuldeep S. Meel School of Computing, National University of Singapore |
| Pseudocode | Yes | The pseudocode of Teq is given in Algorithm 1. Algorithm 1 Teq(ϕ1, w1, ϕ2, w2, ε, η, δ) |
| Open Source Code | Yes | The accompanying tool, available open source, can be found at https://github.com/meelgroup/teq. |
| Open Datasets | Yes | We conducted experiments on a range of publicly available benchmarks arising from sampling and counting tasks4. Our dataset contained 100 d-DNNF circuits with weights. Footnote 4: https://zenodo.org/record/3793090 |
| Dataset Splits | No | The paper describes experiments to evaluate the Teq algorithm on various benchmarks, not the training and validation of a machine learning model, hence standard train/validation/test splits are not applicable or described. |
| Hardware Specification | Yes | Our experiments were conducted on a high performance compute cluster with Intel Xeon(R) E5-2690 v3@2.60GHz CPU cores. |
| Software Dependencies | No | The paper states that the prototype was implemented in Python and uses WAPS3 [17], but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | We set the closeness parameter ε, farness parameter η and confidence δ for Teq to be 0.01, 0.2 and 0.01, respectively. The chosen parameters imply that if the input pair of probabilistic circuits are 0.01 close in d T V , then Teq returns Accept with probability atleast 0.99, otherwise if the circuits are 0.2 far in d T V , the algorithm returns Reject with probability at least 0.99. The number of samples required for Teq (indicated by the variable m as on line 2 of Algorithm 1) depends only on ε, η, δ and for the values we have chosen, we find that we require m = 294 samples. |