Robust Estimation of Tree Structured Gaussian Graphical Models
Authors: Ashish Katiyar, Jessica Hoffmann, Constantine Caramanis
ICML 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | The paper primarily presents theoretical results through theorems (Theorem 1 to Theorem 6) and proposes an algorithm with a proven time complexity. While it includes 'Examples and Illustrations' in Section 4, these are numerical illustrations of the theorems rather than empirical studies involving data analysis, performance metrics, or hypothesis validation. Section 6 'Finite Sample Setting' discusses theoretical sample complexity. |
| Researcher Affiliation | Academia | 1Department of Electrical and Computer Engineering, The University of Texas at Austin, Texas, USA. |
| Pseudocode | No | Section 5 describes the algorithm in prose, but the actual pseudocode blocks are stated to be in Appendix E ('In Appendix E, we give the pseudo-code, proof of correctness and prove that this function is O(n2)'), which is not part of the provided text. |
| Open Source Code | No | The paper does not provide any statement or link indicating the availability of open-source code for the described methodology. |
| Open Datasets | No | The paper discusses 'finite number of noisy samples' and 'sample covariance matrix' in the context of theoretical analysis in Section 6, but it does not mention or provide access information for a specific, publicly available dataset used for training or any other purpose. |
| Dataset Splits | No | The paper is theoretical and focuses on mathematical proofs and algorithm design, not empirical experiments. Therefore, it does not provide details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for any computations or illustrations. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any specific experimental setup details such as hyperparameters, optimizer settings, or other system-level training configurations, as it does not report on empirical experiments. |