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].
Differentially Private Algorithms for Learning Mixtures of Separated Gaussians
Authors: Gautam Kamath, Or Sheffet, Vikrant Singhal, Jonathan Ullman
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this work, we give new algorithms for learning the parameters of a high-dimensional, well separated, Gaussian mixture model subject to the strong constraint of differential privacy. In particular, we give a differentially private analogue of the algorithm of Achlioptas and Mc Sherry (COLT 2005). Our algorithm has two key properties not achieved by prior work: (1) The algorithm s sample complexity matches that of the corresponding non-private algorithm up to lower order terms in a wide range of parameters. (2) The algorithm requires very weak a priori bounds on the parameters of the mixture. |
| Researcher Affiliation | Academia | Gautam Kamath David R. Cheriton School of Computer Science University of Waterloo Waterloo, ON, Canada N2L 3G1 EMAIL Or Sheffet Department of Computer Science, Faculty of Exact Sciences Bar-Ilan University Ramat-Gan, 5290002 Israel EMAIL Vikrant Singhal Khoury College of Computer Sciences Northeastern University 360 Huntington Ave., Boston, MA 02115 EMAIL Jonathan Ullman Khoury College of Computer Sciences Northeastern University 360 Huntington Ave., Boston, MA 02115 EMAIL |
| Pseudocode | Yes | Algorithm 1: Private Gaussian Mixture Partitioning RPGMP(X; k, R, wmin, σmin, σmax, ε, δ) Algorithm 2: Privately Learn Gaussian Mixture PGME(X; k, R, wmin, σmin, σmax, ε, δ, β) |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code is available. |
| Open Datasets | No | The paper is theoretical and focuses on algorithmic design and analysis of sample complexity, rather than empirical evaluation on a specific publicly available dataset. |
| Dataset Splits | No | The paper is theoretical and does not describe dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any hardware specifications for running experiments. |
| Software Dependencies | No | The paper is theoretical and provides algorithms in pseudocode but does not specify any software dependencies with version numbers for implementation. |
| Experiment Setup | No | The paper is theoretical and does not describe experimental setup details such as hyperparameters or training configurations. |