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].
Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms
Authors: Thomas Berrett, Cristina Butucea
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We find separation rates for testing multinomial or more general discrete distributions under the constraint of α-local differential privacy. We construct efficient randomized algorithms and test procedures, in both the case where only noninteractive privacy mechanisms are allowed and also in the case where all sequentially interactive privacy mechanisms are allowed. We prove general information theoretical bounds that allow us to establish the optimality of our algorithms among all pairs of privacy mechanisms and test procedures, in most usual cases. |
| Researcher Affiliation | Academia | Berrett Thomas CREST, ENSAE, IP Paris 5, avenue Henry le Chatelier 91120 Palaiseau Cedex, FRANCE EMAIL Butucea Cristina CREST, ENSAE, IP Paris 5, avenue Henry le Chatelier 91120 Palaiseau Cedex, FRANCE EMAIL |
| Pseudocode | No | The paper describes algorithms and procedures in descriptive text and mathematical formulas but does not include structured pseudocode blocks or clearly labeled 'Algorithm' sections. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide links to a code repository. |
| Open Datasets | No | This is a theoretical paper focused on deriving optimal separation rates and proving information theoretical bounds for statistical testing. It does not conduct experiments with empirical datasets, therefore, there is no mention of publicly available or open datasets. |
| Dataset Splits | No | This is a theoretical paper focused on deriving optimal separation rates and proving information theoretical bounds. It does not conduct experiments with empirical datasets, and therefore does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | This is a theoretical paper that does not involve computational experiments requiring specific hardware. Therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This is a theoretical paper that does not involve computational experiments requiring specific software. Therefore, no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | This is a theoretical paper focused on deriving optimal separation rates and proving information theoretical bounds. It does not describe an experimental setup, hyperparameters, or system-level training settings. |