Private Identity Testing for High-Dimensional Distributions
Authors: Clément L. Canonne, Gautam Kamath, Audra McMillan, Jonathan Ullman, Lydia Zakynthinou
NeurIPS 2020 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution is to give novel algorithms for hypothesis testing of high-dimensional distributions with improved sample complexity. In particular, we give differentially private algorithms for the following fundamental problems: |
| Researcher Affiliation | Collaboration | Clément Canonne IBM Research, Almaden EMAIL; Gautam Kamath Cheriton School of Computer Science University of Waterloo EMAIL; Audra Mc Millan Khoury College of Computer Sciences, Northeastern University Department of Computer Science, Boston University EMAIL; Jonathan Ullman Khoury College of Computer Sciences Northeastern University EMAIL; Lydia Zakynthinou Khoury College of Computer Sciences Northeastern University EMAIL |
| Pseudocode | Yes | Algorithm 1 LIPEXTTEST; Algorithm 2 Private Uniformity Testing via Lipschitz Extension |
| Open Source Code | No | The paper does not provide any links to open-source code or state that code is made available. |
| Open Datasets | No | The paper discusses theoretical sample complexity for distributions (e.g., "product distribution P over { 1}d", "multivariate Gaussian P in Rd") rather than empirical evaluation on specific named datasets. Therefore, no access information for a public dataset is provided. |
| Dataset Splits | No | The paper does not mention any training, validation, or test dataset splits, as it focuses on theoretical analysis rather than empirical experimentation. |
| Hardware Specification | No | The paper focuses on theoretical algorithms and their sample complexity. It does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and describes algorithms and proofs. It does not list any software dependencies or version numbers. |
| Experiment Setup | No | The paper presents theoretical algorithms and their analysis (e.g., sample complexity). It does not describe any experimental setup details such as hyperparameters, training configurations, or system-level settings. |