Synthetic Data Generators -- Sequential and Private
Authors: Olivier Bousquet, Roi Livni, Shay Moran
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We study the sample complexity of private synthetic data generation over an unbounded sized class of statistical queries, and show that any class that is privately proper PAC learnable admits a private synthetic data generator (perhaps nonefficient). Previous work on synthetic data generators focused on the case that the query class D is finite and obtained sample complexity bounds that scale logarithmically with the size |D|. Here we construct a private synthetic data generator whose sample complexity is independent of the domain size, and we replace finiteness with the assumption that D is privately PAC learnable (a formally weaker task, hence we obtain equivalence between the two tasks). |
| Researcher Affiliation | Collaboration | Olivier Bousquet Google Research, Brain team Zürich obousquet@google.com Roi Livni Tel-Aviv University Tel-Aviv, Israel rlivni@tauex.tau.ac.il Shay Moran Technion Haifa, Israel shaymoran1@gmail.com |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper that focuses on mathematical proofs and characterizations, and therefore does not use publicly available datasets for training empirical models. |
| Dataset Splits | No | This is a theoretical paper and does not conduct experiments with data, thus it does not provide dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide any specific hardware details as it is a theoretical work and does not involve empirical experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers, as it is a theoretical work. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameter values or training configurations. |