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..
A Computational Separation between Private Learning and Online Learning
Authors: Mark Bun
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | A recent line of work has shown a qualitative equivalence between differentially private PAC learning and online learning: A concept class is privately learnable if and only if it is online learnable with a finite mistake bound. However, both directions of this equivalence incur significant losses in both sample and computational efficiency. Studying a special case of this connection, Gonen, Hazan, and Moran (Neur IPS 2019) showed that uniform or highly sample-efficient pure-private learners can be time-efficiently compiled into online learners. We show that, assuming the existence of one-way functions, such an efficient conversion is impossible even for general pure-private learners with polynomial sample complexity. This resolves a question of Neel, Roth, and Wu (FOCS 2019). |
| Researcher Affiliation | Academia | Mark Bun Department of Computer Science Boston University Boston, MA 02215 EMAIL |
| Pseudocode | Yes | Algorithm 1 Pure Private Learner for OWSd |
| Open Source Code | No | The paper does not provide a link to source code for the methodology described. It links to the full version of the paper on arXiv. |
| Open Datasets | No | The paper is theoretical and does not use a publicly available dataset for empirical training or evaluation. It defines the PAC model and sample complexity theoretically. |
| Dataset Splits | No | The paper is theoretical and does not perform empirical experiments requiring training/validation/test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not report on empirical experiments; therefore, no hardware specifications are provided. |
| Software Dependencies | No | The paper is theoretical and does not describe software dependencies with version numbers for empirical experiments. |
| Experiment Setup | No | The paper is theoretical and does not provide specific experimental setup details such as hyperparameters or training configurations for empirical experiments. |