Approximate Implication for Probabilistic Graphical Models
Authors: Batya Kenig
JAIR 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we prove new negative and positive results concerning this problem. We prove that separators in undirected PGMs do not necessarily represent approximate CIs. ... We also establish improved approximation guarantees for independence relations derived from marginal and saturated CIs. |
| Researcher Affiliation | Academia | Batya Kenig EMAIL Technion, Israel Institute of Technology Haifa, Israel |
| Pseudocode | No | The paper contains mathematical definitions, theorems, and proofs but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not contain any statements about open-source code availability, links to repositories, or mention of code in supplementary materials. |
| Open Datasets | No | The paper presents theoretical proofs and mathematical constructs such as probability distributions (e.g., the parity distribution in Section 6.2) and does not utilize or refer to any publicly available or open datasets for experimental purposes. |
| Dataset Splits | No | The paper focuses on theoretical research and does not involve the use of empirical datasets, thus no dataset split information is provided. |
| Hardware Specification | No | The paper focuses on theoretical research and does not report on experimental setups, therefore no hardware specifications are mentioned. |
| Software Dependencies | No | The paper primarily presents theoretical results and proofs, without detailing any software implementations or dependencies with specific version numbers. |
| Experiment Setup | No | The paper focuses on theoretical derivations and proofs, not experimental evaluation, so no specific experimental setup details or hyperparameter values are provided. |