Rate-Agnostic (Causal) Structure Learning
Authors: Sergey Plis, David Danks, Cynthia Freeman, Vince Calhoun
NeurIPS 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply these algorithms to data from simulations to gain insight into the challenge of undersampling. We finish in Section 4 by exploring their performance on synthetic data. |
| Researcher Affiliation | Academia | Sergey Plis The Mind Research Network, Albuquerque, NM s.m.plis@gmail.com David Danks Carnegie-Mellon University Pittsburgh, PA ddanks@cmu.edu Cynthia Freeman The Mind Research Network, CS Dept., University of New Mexico Albuquerque, NM cynthiaw2004@gmail.com Vince Calhoun The Mind Research Network ECE Dept., University of New Mexico Albuquerque, NM vcalhoun@mrn.org |
| Pseudocode | Yes | a: RASLre algorithm |
| Open Source Code | No | The paper does not contain an explicit statement about releasing code, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper uses 'synthetic data' and 'simulated graphs' that were generated for the experiments (e.g., 'generated 100 random G1'), but it does not refer to a publicly available dataset nor provides access information for one. |
| Dataset Splits | No | The paper discusses the use of synthetic and simulated data but does not specify explicit training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not specify any hardware details such as CPU/GPU models, memory, or specific computing environments used for running the experiments. |
| Software Dependencies | No | The paper mentions SVAR and VAR models, referencing a book, but does not specify any software names with version numbers for implementation details. |
| Experiment Setup | No | The paper describes aspects of data generation (e.g., 'generated a random transition matrix A by sampling weights... and controlling system stability'), but it does not provide specific experimental setup details such as hyperparameters for the SVAR optimization or other model-specific training settings. |