Lower Bounds for Approximate Knowledge Compilation
Authors: Alexis de Colnet, Stefan Mengel
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we formalize two notions of approximation: weak approximation which has been studied before in the decision diagram literature and strong approximation which has been used in recent algorithmic results. We then show lower bounds for approximation by d-DNNF, complementing the positive results from the literature. |
| Researcher Affiliation | Academia | Alexis de Colnet and Stefan Mengel CRIL, CNRS & Univ Artois decolnet@cril.fr, mengel@cril.fr |
| Pseudocode | No | The paper does not contain any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code or links to a code repository for the methodology described. |
| Open Datasets | No | This is a theoretical paper focusing on mathematical proofs and doesn't utilize empirical datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not describe experimental validation dataset splits. |
| Hardware Specification | No | This is a theoretical paper, and thus no hardware specifications for running experiments are mentioned. |
| Software Dependencies | No | This is a theoretical paper, and therefore, no specific software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper, and as such, it does not detail any experimental setup, hyperparameters, or training configurations. |