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..
Private Learning Implies Online Learning: An Efficient Reduction
Authors: Alon Gonen, Elad Hazan, Shay Moran
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper we resolve this open question in the context of pure differential privacy. We derive an ef๏ฌcient black-box reduction from differentially private learning to online learning from expert advice. |
| Researcher Affiliation | Collaboration | Alon Gonen University of California San Diego EMAIL Elad Hazan Princeton University and Google AI Princeton EMAIL Shay Moran Google AI Princeton EMAIL |
| Pseudocode | Yes | Algorithm 1 Weak online learner for oblivious adversaries |
| Open Source Code | No | The paper does not provide any statements about releasing source code or links to a code repository. |
| Open Datasets | No | The paper is a theoretical work and does not describe or use specific datasets for empirical training or evaluation. |
| Dataset Splits | No | The paper is theoretical and does not mention specific training, validation, or test dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not specify any software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not detail any experimental setup, hyperparameters, or training configurations. |