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 Model Personalization
Authors: Prateek Jain, John Rush, Adam Smith, Shuang Song, Abhradeep Guha Thakurta
NeurIPS 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give new algorithms for this setting, analyze their accuracy on specific data distributions, and test our results empirically. |
| Researcher Affiliation | Collaboration | Prateek Jain Google Research EMAIL Keith Rush Google Research EMAIL Adam Smith Boston University EMAIL Shuang Song Google Research EMAIL Abhradeep Thakurta Google Research EMAIL |
| Pseudocode | Yes | Algorithm 1 APriv-Alt Min: Differentially Private Alternating Minimization Meta-algorithm |
| Open Source Code | No | The paper does not provide a statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | No | The paper mentions using 'synthetic data' but does not provide any link, DOI, or formal citation for public access to this data. |
| Dataset Splits | No | The paper describes generating synthetic data and evaluating population MSE, but it does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts for overall model evaluation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | We set the number of users n = 50, 000, number of samples per user m = 10, data dimension d = 50 and rank k = 2. We sample x, U and v from Gaussian distributions, and the field noise σF of target y is set to be 0.01. We normalize U to unit norm. We run Algorithm 1 with full batch, i.e., T = 1 and for multiple epochs. [...] We fix the clipping norm to be 10 4 and pick the optimal the number of epochs in {1, 2, 5, 10}. |