Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Private Learning Implies Online Learning: An Efficient Reduction
Authors: Alon Gonen, Elad Hazan, Shay Moran
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,095 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,649 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| 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. |