Learning from Mixtures of Private and Public Populations
Authors: Raef Bassily, Shay Moran, Anupama Nandi
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our construction outputs a hypothesis with excess true error α using an input sample of size O(d2/ϵα) in the realizable setting, and a sample of size O(d2 max{1/ϵ, 1/α}) in the agnostic setting. Our algorithm is an improper learner; specifically, the output hypothesis is given by the intersection of at most d halfspaces. ... The main goal of this work is to introduce a new, more flexible framework for differentially private learning that captures more realistic scenarios than prior works. |
| Researcher Affiliation | Academia | Raef Bassily Department of Computer Science & Engineering The Ohio State University bassily.1@osu.edu; Shay Moran Department of Mathematics Technion Israel Institute of Technology smoran@technion.ac.il; Anupama Nandi Department of Computer Science & Engineering The Ohio State University nandi.10@osu.edu |
| Pseudocode | Yes | Algorithm 1 AConstr Half: Construction of the family e Cpub halfspaces; Algorithm 2 ALearn Half: PPM Learning of Halfspaces |
| Open Source Code | No | The paper makes no mention of open-source code availability or provides any links to code repositories. It refers to a 'full version [BMN20]' for additional details, which is a reference to an arXiv preprint, not a code release. |
| Open Datasets | No | The paper does not use or refer to any specific publicly available dataset. It discusses theoretical distributions (Dpriv, Dpub) and uses illustrative examples like medical studies or credit-worthiness, but no actual dataset access information is provided. |
| Dataset Splits | No | The paper is theoretical and focuses on sample complexity bounds for learning. It does not describe any experimental setup involving training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not conduct empirical experiments, so it does not specify any hardware used. |
| Software Dependencies | No | The paper is theoretical and does not conduct empirical experiments, so it does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and focuses on algorithm design and analysis (e.g., sample complexity). It does not describe an experimental setup with specific hyperparameters, training configurations, or system-level settings. |