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..
Sharp Bounds for Generalized Uniformity Testing
Authors: Ilias Diakonikolas, Daniel M. Kane, Alistair Stewart
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We establish tight bounds on the sample complexity of generalized uniformity testing. In more detail, we present a computationally efficient tester whose sample complexity is optimal, within constant factors, and a matching worst-case information-theoretic lower bound. Specifically, we show that the sample complexity of generalized uniformity testing is Θ 1/(ϵ4/3 p 3) + 1/(ϵ2 p 2) . |
| Researcher Affiliation | Academia | Ilias Diakonikolas University of Southern California EMAIL Daniel M. Kane University of California, San Diego EMAIL Alistair Stewart University of Southern California EMAIL |
| Pseudocode | Yes | Algorithm 1 Algorithm for Rough Moment Estimation, Algorithm 2 Algorithm for Moment Estimation, Algorithm 3 Algorithm for Large ϵ, Algorithm 4 Algorithm for Small Epsilon, Algorithm 5 The Full Tester |
| Open Source Code | No | The paper focuses on theoretical bounds and algorithms; no statement about providing open-source code for the described methodology. |
| Open Datasets | No | This is a theoretical paper presenting algorithms and proofs; it does not use empirical datasets for training. |
| Dataset Splits | No | This is a theoretical paper and does not involve empirical data splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not describe experimental procedures that would require hardware specifications. |
| Software Dependencies | No | The paper is theoretical and does not provide details on software dependencies or versions. |
| Experiment Setup | No | The paper is theoretical and does not detail an experimental setup, hyperparameters, or training configurations. |