Learnability of Parameter-Bounded Bayes Nets
Authors: Arnab Bhattacharyya, Davin Choo, Sutanu Gayen, Dimitrios Myrisiotis
AAAI 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our two main contributions are that we extend the hardness result of Chickering, Heckerman, and Meek (2004) and generalize the finite sample complexity result of Brustle, Cai, and Daskalakis (2020). ... We then formally prove Theorem 1.3 and Theorem 1.4 in Section 4 and Section 5, respectively. |
| Researcher Affiliation | Academia | 1University of Warwick, United Kingdom 2Harvard University, USA 3IIT Kanpur, India 4CNRS@CREATE LTD., Singapore |
| Pseudocode | No | The paper describes algorithms (e.g., Learner, Reduction, Checker) in prose within the technical overview and proofs, but does not present them in a structured pseudocode block or clearly labeled algorithm section. |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code for the methodology described, nor does it provide any links to code repositories. |
| Open Datasets | No | The paper is theoretical and focuses on learnability and sample complexity bounds for Bayes nets. It discusses working with 'sample access to a distribution P' but does not conduct experiments on specific, named public datasets with access information. Therefore, no concrete access information for open datasets is provided. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments with specific datasets. Therefore, there is no mention of training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or results that would require specific hardware. Therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and focuses on mathematical proofs and algorithmic analysis. It does not describe any implementation details or experiments that would require specific software dependencies or version numbers. |
| Experiment Setup | No | The paper presents theoretical results concerning NP-hardness and sample complexity. It does not include any empirical experiments, and therefore, no experimental setup details, hyperparameters, or training configurations are provided. |