Probabilistic Generating Circuits - Demystified
Authors: Sanyam Agarwal, Markus Bläser
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | One main insight of this work is that the negative weights are the cause for the power of PGCs and not the different representation. PGCs are PCs in disguise: we show how to transform any PGC on binary variables into a PC with negative weights with only polynomial blowup. As our second main result, we show that there is a good reason for this: we prove that PGCs for categorical variables with larger image size do not support tractable marginalization unless NP = P. |
| Researcher Affiliation | Academia | 1Saarland University, Saarland Informatics Campus, Saarbr ucken, Germany. Correspondence to: Sanyam Agarwal <agarwal@cs.uni-saarland.de>, Markus Bl aser <mblaeser@cs.uni-saarland.de>. |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code is made publicly available for the described methodology. |
| Open Datasets | No | The paper focuses on theoretical analysis and does not use or train on datasets, thus no dataset availability information is provided. |
| Dataset Splits | No | The paper focuses on theoretical analysis and does not involve data processing with training, validation, or test splits. |
| Hardware Specification | No | The paper is theoretical and does not describe computational experiments that would require specific hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not describe computational experiments that would require specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setups, hyperparameters, or training configurations. |