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..
Greedy Poisson Rejection Sampling
Authors: Gergely Flamich
NeurIPS 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically verify our theorems, demonstrating that GPRS significantly outperforms the current state-of-the-art method, A* coding. Our code is available at https:// github.com/gergely-flamich/greedy-poisson-rejection-sampling. |
| Researcher Affiliation | Academia | Gergely Flamich Department of Engineering University of Cambridge EMAIL |
| Pseudocode | Yes | Algorithm 1: Generating a (λ, PX|T )-Poisson process. Algorithm 2: Standard rejection sampler. Algorithm 3: Greedy Poisson rejection sampler. Algorithm 4: Parallel GPRS with J available threads. Algorithm 5: Branch-and-bound GPRS on R with unimodal r Algorithm 6: Branch-and-bound GPRS with splitting function. |
| Open Source Code | Yes | Our code is available at https:// github.com/gergely-flamich/greedy-poisson-rejection-sampling. |
| Open Datasets | No | The paper does not use a named, publicly available dataset with a link or formal citation. It generates data based on specified distributions (e.g., Gaussian) for its experiments. |
| Dataset Splits | No | The paper conducts experiments by simulating data from specified distributions. It does not mention traditional train/validation/test dataset splits as one would for fixed datasets. |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU models, CPU types, or memory used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | We use a setup similar to the one used by Theis & Yosri (2022). Concretely, we assume the following model for correlated random variables x, µ: Pµ = N(0, 1) Px|µ = N(µ, σ2). |