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].
Privately Learning Mixtures of Axis-Aligned Gaussians
Authors: Ishaq Aden-Ali, Hassan Ashtiani, Christopher Liaw
NeurIPS 2021 | Venue PDF | 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 EMAIL Hassan Ashtiani Department of Computing and Software Mc Master University EMAIL Christopher Liaw Department of Computer Science University of Toronto EMAIL |
| 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. |