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].
A General Framework for Symmetric Property Estimation
Authors: Moses Charikar, Kirankumar Shiragur, Aaron Sidford
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We performed two different sets of experiments for entropy estimation one to compare performance guarantees and the other to compare running times. In our pseudo PML approach, we divide the samples into two parts. We run the empirical estimate on one (this is easy) and the PML estimate on the other. |
| Researcher Affiliation | Academia | Moses Charikar Stanford University EMAIL Kirankumar Shiragur Stanford University EMAIL Aaron Sidford Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 General Framework for Symmetric Property Estimation |
| Open Source Code | Yes | Our code is available at https://github.com/shiragur/Code For Pseudo PML.git |
| Open Datasets | No | The paper uses synthetic distributions (Mix 2 Uniforms, Zipf(0.5), Zipf(1)) with a specified domain size (N=10^5) for experiments, rather than explicitly referencing a publicly available dataset with concrete access information (link, DOI, formal citation). |
| Dataset Splits | Yes | In our pseudo PML approach, we divide the samples into two parts. ... Let x2n = (xn/2, xn/2), where xn/2 represent ο¬rst and last n samples of x2n respectively. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'the heuristic algorithm in [PJW17]', but it does not specify any software dependencies with version numbers (e.g., Python, specific libraries, or exact versions of [PJW17]'s implementation). |
| Experiment Setup | Yes | In our algorithm we pick threshold = 18 (same as [WY16a]) and our set F = [0, 18] (input of Algorithm 1), i.e. we use the PML estimate on frequencies 18 and empirical estimate on the rest. |