Privately Learning Mixtures of Axis-Aligned Gaussians
Authors: Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that e O(k2d log3/2(1/δ)/α2ε) samples are sufficient to learn a mixture of k axis-aligned Gaussians in Rd to within total variation distance α while satisfying (ε, δ)-differential privacy. To prove our results, we design a new technique for privately learning mixture distributions. If you ran experiments... [N/A] |
| Researcher Affiliation | Academia | Ishaq Aden-Ali Department of Computing and Software Mc Master University adenali@mcmaster.ca Hassan Ashtiani Department of Computing and Software Mc Master University zokaeiam@mcmaster.ca Christopher Liaw Department of Computer Science University of Toronto cvliaw@cs.toronto.edu |
| Pseudocode | Yes | Algorithm 1: Univariate-Mean-Decoder(β, γ, ε, δ, eσ, D). |
| Open Source Code | No | If you are including theoretical results... 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 | This is a theoretical paper that focuses on mathematical proofs and algorithms, not empirical evaluation on datasets. The ethics statement indicates 'N/A' for experiments, implying no specific dataset was used or made available. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical experiments with data; therefore, there are no training, validation, or test splits mentioned. |
| Hardware Specification | No | This paper is theoretical and does not conduct experiments, therefore, no hardware specifications are mentioned. |
| Software Dependencies | No | This paper is theoretical and does not describe software implementation or dependencies with version numbers. |
| Experiment Setup | No | This paper is theoretical and does not describe an experimental setup with hyperparameters or training settings. |