Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in [1].
Scalable Inference of Sparsely-changing Gaussian Markov Random Fields
Authors: Salar Fattahi, Andres Gomez
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we evaluate the performance of the proposed estimator in synthetically generated massive datasets, and a case study on the correlation network inference in stock markets. and Figure 7 depicts TPR, FPR, and the max-norm error of the estimated parameters, as well as the runtime of our algorithm for different values of d with and without parallelization. |
| Researcher Affiliation | Academia | Salar Fattahi Department of Industrial & Operations Engineering University of Michigan Ann Arbor, MI 48109 EMAIL Andrés Gómez Department of Industrial & System Engineering University of Southern California Los Angeles, CA 90089 EMAIL |
| Pseudocode | Yes | Algorithm 1 Greedy(l, u, , T) and Algorithm 2 Algorithm for solving (7) |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper mentions using 'synthetically generated massive datasets' and 'daily changes for 214 securities from 1990/01/04 to 2017/08/10'. While it references a NASDAQ chart [2], it does not provide concrete access information (link, DOI, repository, or formal citation for the dataset itself) for either the synthetic or real-world data used in the experiments. |
| Dataset Splits | No | The paper does not explicitly specify training, validation, or test dataset splits (e.g., exact percentages or sample counts). It mentions collecting samples but no detailed splitting methodology. |
| Hardware Specification | No | The paper mentions evaluating runtime with different numbers of cores (single, 5, 10 cores) but does not provide specific hardware details such as CPU/GPU models, processor types, or memory used for the experiments. |
| Software Dependencies | No | The paper describes algorithms and mathematical formulations but does not list any specific software dependencies or libraries with version numbers (e.g., Python 3.x, PyTorch 1.x) that were used for implementation. |
| Experiment Setup | Yes | In all of our simulations, the parameters t and λt are chosen directly from the data samples, i.e., without prior knowledge of the true solution, via Bayesian Inference Criterion (BIC) [31, 13]. and The regularization parameter γ in the objective function of (3) is set to 0.2. and for the choices of 0 = 3, λ0 = 0.16, and γ = 0.9 |