Variational Inference with Coverage Guarantees in Simulation-Based Inference
Authors: Yash Patel, Declan Mcnamara, Jackson Loper, Jeffrey Regier, Ambuj Tewari
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we demonstrate the accurate calibration and high predictive efficiency of CANVI on a suite of simulation-based inference benchmark tasks and an important scientific task: analyzing galaxy emission spectra. |
| Researcher Affiliation | Academia | 1Department of Statistics, University of Michigan, Ann Arbor, USA. Correspondence to: Yash Patel <yppatel@umich.edu>. |
| Pseudocode | Yes | The full CANVI framework is provided in Algorithm 1. |
| Open Source Code | Yes | Details are provided in Appendix G, and code is available at https: //github.com/yashpatel5400/canvi.git. |
| Open Datasets | Yes | We evaluate on the standard SBI benchmark tasks, highlighted in (Delaunoy et al., 2023). For full descriptions of the tasks, refer to Appendix F. The benchmark tasks are a subset of those provided by (Lueckmann et al., 2021). The PROVABGS emulator (Section 4.3) was trained to minimize the MSE using normalized simulated PROVABGS outputs with fixed log stellar mass parameter (Hahn et al., 2023). |
| Dataset Splits | Yes | CANVI was applied to an NPE, in which DC was taken to be 10% of the simulation budgets and the remainder used for training. We take DR to be the same size as DC, i.e. |DR| = NC. Algorithm 1: DC, DR P(X, Θ), DT P(X). |
| Hardware Specification | Yes | Training these models required between 10 minutes and two hours using an Nvidia RTX 2080 Ti GPUs for each of the SBI tasks. |
| Software Dependencies | No | The paper mentions "Py Torch (Paszke et al., 2019)", "Neural Spline Flow architecture", "Adam (Kingma & Ba, 2014)", and "nflows: normalizing flows in Py Torch, November 2020a". While these indicate the software used, specific version numbers (e.g., PyTorch 1.x.x) are not explicitly provided. |
| Experiment Setup | Yes | Optimization was done using Adam (Kingma & Ba, 2014) with a learning rate of 10 3 over 5,000 training steps. Specific architecture hyperparameter choices were taken to be the defaults from (Durkan et al., 2020a) and are available in the code. All three methods were trained for 10,000 steps using the Adam optimizer with learning rate 0.0001. |