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].
Nearly-Tight Bounds for Testing Histogram Distributions
Authors: Clément L Canonne, Ilias Diakonikolas, Daniel Kane, Sihan Liu
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is a near-characterization of the sample complexity of the histogram testing problem. Specifically, we provide (1) a sample near-optimal and computationally efficient testing algorithm for the problem, and (2) a nearly-matching sample complexity lower bound (within logarithmic factors).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)? [N/A] |
| Researcher Affiliation | Academia | Clément L. Canonne University of Sydney EMAIL Ilias Diakonikolas University of Wisconsin-Madison EMAIL Daniel M. Kane University of California, San Diego EMAIL Sihan Liu University of California, San Diego EMAIL |
| Pseudocode | Yes | Algorithm 1 Learn-And-Sieve |
| Open Source Code | No | 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)? [N/A] |
| Open Datasets | No | The paper is theoretical and focuses on sample complexity bounds and algorithm design; it does not use or refer to any specific publicly available dataset for empirical evaluation. |
| Dataset Splits | No | The paper is theoretical and does not include empirical experiments, thus no training, validation, or test dataset splits are provided. The authors also marked 'N/A' for questions related to experimental details. |
| Hardware Specification | No | The paper is theoretical and does not describe any empirical experiments, therefore no hardware specifications are provided. The authors marked 'N/A' for questions about experimental details. |
| Software Dependencies | No | The paper is theoretical and does not describe any empirical experiments, therefore no software dependencies with version numbers are provided. |
| Experiment Setup | No | The paper is theoretical and does not include an empirical experimental setup, therefore no hyperparameters or system-level training settings are provided. |