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].
Private Estimation with Public Data
Authors: Alex Bie, Gautam Kamath, Vikrant Singhal
NeurIPS 2022 | Venue PDF | 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 EMAIL Gautam Kamath University of Waterloo EMAIL Vikrant Singhal University of Waterloo EMAIL |
| 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. |