Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Authors: Diana Cai, Ryan P. Adams
NeurIPS 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,684 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,557 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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). |