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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Authors: Diana Cai, Ryan P. Adams
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We apply the pseudo-marginal multi-fidelity MCMC approach to several problems, including log-Gaussian Cox process modeling, Bayesian ODE system identification, PDE-constrained optimization, and Gaussian process parameter inference. |
| Researcher Affiliation | Academia | Diana Cai Department of Computer Science Princeton University EMAIL Ryan P. Adams Department of Computer Science Princeton University EMAIL |
| Pseudocode | Yes | Algorithm 1 Multi-fidelity Monte Carlo with sign-correction |
| Open Source Code | Yes | 3. If you ran experiments...(a) Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [Yes] See Appendix F. |
| Open Datasets | Yes | We apply multi-fidelity and single-fidelity ESS algorithms to a coal mining disasters data set (Carlin et al. [9]). |
| Dataset Splits | No | The paper does not explicitly provide training/test/validation dataset splits. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used to run its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | In all experiments, we use a random-walk M-H update to sample from the conditional K|θ, and truncation distribution µ(K) = geometric(K; γ0). |