Identifiability Analysis of Linear ODE Systems with Hidden Confounders
Authors: Yuanyuan Wang, Biwei Huang, Wei Huang, Xi Geng, Mingming Gong
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our theoretical results, we perform a series of simulations, which support and substantiate our findings. 5 Simulations. To evaluate the validity of the identifiability conditions established in Section 3 and 4, we present the results of simulations. |
| Researcher Affiliation | Academia | Yuanyuan Wang The University of Melbourne yuanyuanw2@student.unimelb.edu.au Biwei Huang University of California, San Diego bih007@ucsd.edu Wei Huang The University of Melbourne wei.huang@unimelb.edu.au Xi Geng The University of Melbourne xi.geng@unimelb.edu.au Mingming Gong The University of Melbourne mingming.gong@unimelb.edu.au |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | Yes | We provide all experimental details in Section 5 and include the code in the supplemental material. |
| Open Datasets | No | Observations are simulated from the true ODE systems for each case, with n equally-spaced observations generated from the time interval [0, 1] for each trajectory, and we only keep the values of the observable variables x. |
| Dataset Splits | No | The paper describes generating 'n equally-spaced observations' from simulated ODE systems but does not explicitly define or refer to standard training, validation, or test dataset splits. |
| Hardware Specification | No | Since our experiments solely consist of simulations designed to validate our theoretical findings, the computational resources employed are not a consideration for our research objectives. |
| Software Dependencies | No | The 'least_squares' function from the 'scipy.optimize' Python module, with default hyperparameter settings, is utilized for implementation. |
| Experiment Setup | Yes | The dimensions of both observable variables, d, and latent variables, p, are set to 3. Parameter initialization is performed near the true values to promote convergence to the global minimum. Specifically, for the η-(un)identifiable cases, initial parameter values are set to the true parameters plus a random value drawn from a uniform distribution U( 0.1, 0.1) for each replication. For {ηi}p 1-(un)identifiable cases, initial parameter values are set to the true values plus a random value from U( 0.3, 0.3). |