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..
Model-Agnostic Private Learning
Authors: Raef Bassily, Om Thakkar, Abhradeep Guha Thakurta
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We design differentially private learning algorithms that are agnostic to the learning model assuming access to a limited amount of unlabeled public data. First, we provide a new differentially private algorithm for answering a sequence of m online classification queries (given by a sequence of m unlabeled public feature vectors) based on a private training set. Our algorithm follows the paradigm of subsample-and-aggregate... We show that our algorithm makes a conservative use of the privacy budget... In the PAC model, we analyze our construction and prove upper bounds on the sample complexity for both the realizable and the non-realizable cases. Similar to non-private sample complexity, our bounds are completely characterized by the VC dimension of the concept class. |
| Researcher Affiliation | Academia | Department of Computer Science & Engineering, The Ohio State University. EMAIL Department of Computer Science, Boston University. EMAIL Department of Computer Science, University of California Santa Cruz. EMAIL |
| Pseudocode | Yes | Algorithm 1 Astab [26]: Private release of a classification query via distance to instability; Algorithm 2 Abin Clas: Private Online Binary Classification via subsample and aggregate; and sparse vector; Algorithm 3 APriv: Private Learner |
| Open Source Code | No | The paper does not provide any links to open-source code or explicitly state that code for the described methodology is being released. |
| Open Datasets | No | The paper is theoretical and does not use specific, named, publicly available datasets. It refers to a 'private labeled dataset denoted by D' and 'unlabeled public data' but does not provide access information or citations for them. |
| Dataset Splits | No | The paper is theoretical and does not conduct experiments that would require specific training/validation/test dataset splits. It mentions 'standard validation techniques' in a remark, but not in the context of its own empirical setup. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or the hardware used to run experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers for experimental reproducibility. |
| Experiment Setup | No | The paper is theoretical and does not describe specific experimental setup details, hyperparameters, or training configurations. |