Private Estimation with Public Data
Authors: Alex Bie, Gautam Kamath, Vikrant Singhal
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In Appendix D we present some proof-of-concept numerical simulations demonstrating the effectivenss of public data for private estimation. |
| Researcher Affiliation | Academia | Alex Bie University of Waterloo yabie@uwaterloo.ca Gautam Kamath University of Waterloo g@csail.mit.edu Vikrant Singhal University of Waterloo vikrant.singhal@uwaterloo.ca |
| Pseudocode | Yes | Algorithm 1: Public Data Preconditioner Pub Preconditionerβ( e X) |
| Open Source Code | Yes | No license in the repository (https://github.com/twistedcubic/coin-press), however we received permission from the authors to use their code. |
| Open Datasets | No | The paper refers to drawing samples from Gaussian distributions and Gaussian mixtures. It does not use or provide access to named public datasets or benchmarks with explicit citations or URLs. |
| Dataset Splits | No | The paper does not explicitly mention training, validation, or test dataset splits in the context of machine learning experiments. It discusses public and private data samples but not standard ML splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for its numerical simulations or experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers. |
| Experiment Setup | No | The paper states in Appendix D that it presents "proof-of-concept numerical simulations" but does not provide specific experimental setup details such as hyperparameters, learning rates, or batch sizes in the main text. |