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..
Replicable Uniformity Testing
Authors: Sihan Liu, Christopher Ye
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we revisit uniformity testing under the framework of algorithmic replicability [STOC 22], requiring the algorithm to be replicable under arbitrary distributions. While replicability typically incurs a Ļ 2 factor overhead in sample complexity, we obtain a replicable uniformity tester using only O( nĪµ 2Ļ 1) samples. To our knowledge, this is the first replicable learning algorithm with (nearly) linear dependence on Ļ. Lastly, we consider a class of symmetric" algorithms [FOCS 00] whose outputs are invariant under relabeling of the domain [n], which includes all existing uniformity testers (including ours). For this natural class of algorithms, we prove a nearly matching sample complexity lower bound for replicable uniformity testing. |
| Researcher Affiliation | Academia | Sihan Liu University of California San Diego La Jolla, CA EMAIL Christopher Ye University of California San Diego La Jolla, CA EMAIL |
| Pseudocode | Yes | Algorithm 1 RUNIFORMITYTESTER(p, Īµ, Ļ) Input :Sample access to distribution p on domain [n] Parameters :Īµ tolerance, Ļ replicability Output :ACCEPT if p Un is uniform, REJECT if d T V (p, Un) Īµ |
| Open Source Code | No | The paper is theoretical and presents algorithms and proofs. It does not mention providing open-source code for its methodology. The NeurIPS checklist also indicates 'NA' for questions related to code and data availability, confirming no code is provided. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments with datasets, thus no information about public datasets or their access is provided. |
| Dataset Splits | No | The paper is theoretical and does not report on experimental validation, thus no dataset split information for training, validation, or testing is provided. |
| Hardware Specification | No | The paper is theoretical and does not describe any experiments that would require hardware specifications. Therefore, no hardware details are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not implement or describe any software requiring specific dependencies or version numbers. |
| Experiment Setup | No | The paper is purely theoretical and does not include any experimental setup details such as hyperparameters or training configurations. |