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 [1].
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). |