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].
Algorithms for the Communication of Samples
Authors: Lucas Theis, Noureldin Y Ahmed
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We run two sets of empirical experiments to compare the reverse channel coding schemes discussed above. We ο¬rst investigate the effect of hybrid coding on the computational cost of communicating a (truncated) Gaussian sample. We then compare the performance of a wider set of algorithms for the task of approximately simulating a categorical distribution. |
| Researcher Affiliation | Industry | 1Google, London, UK 2Google, Dublin, Ireland. |
| Pseudocode | Yes | Algorithm 1 PFR; Algorithm 2 Hybrid coding; Algorithm 3 Greedy rejection sampling (RS*; Harsha et al., 2007); Algorithm 4 Ordered random coding (ORC) |
| Open Source Code | No | The paper does not provide any links to a code repository or make explicit statements about open-sourcing their code. |
| Open Datasets | No | The paper describes generating samples from specified distributions (Gaussian and Dirichlet-distributed categorical distributions) but does not refer to or provide access information for a pre-existing, publicly available dataset in the conventional sense. |
| Dataset Splits | No | The paper does not explicitly mention training, validation, or test dataset splits, cross-validation, or any other data partitioning strategy. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., programming languages, libraries, frameworks). |
| Experiment Setup | Yes | Appropriate values for wmin and M are provided in Appendix H. |