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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Learning from Mixtures of Private and Public Populations
Authors: Raef Bassily, Shay Moran, Anupama Nandi
NeurIPS 2020 | Venue PDF | 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 EMAIL; Shay Moran Department of Mathematics Technion Israel Institute of Technology EMAIL; Anupama Nandi Department of Computer Science & Engineering The Ohio State University EMAIL |
| 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. |