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.
Locally Private Gaussian Estimation
Authors: Matthew Joseph, Janardhan Kulkarni, Jieming Mao, Steven Z. Wu
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 14,582 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,463 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 | We match these upper bounds with an information-theoretic lower bound showing that our accuracy guarantees are tight up to logarithmic factors for all sequentially interactive locally private protocols. |
| Researcher Affiliation | Collaboration | Matthew Joseph University of Pennsylvania EMAIL Janardhan Kulkarni Microsoft Research Redmond EMAIL Jieming Mao Google Research New York EMAIL Zhiwei Steven Wu University of Minnesota EMAIL |
| Pseudocode | Yes | Algorithm 1 KVGAUSSTIMATE, Algorithm 2 KVAGG1, Algorithm 3 ESTMEAN, Algorithm 4 KVRR2, Algorithm 5 KVAGG2, Algorithm 6 UVGAUSSTIMATE |
| Open Source Code | No | The paper does not provide any statements or links regarding the public availability of source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and focuses on estimation problem with i.i.d. samples from a Gaussian distribution, without referring to any specific publicly available dataset or providing access information for one. |
| Dataset Splits | No | The paper is theoretical and does not involve empirical experiments requiring dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not describe any specific hardware used for running 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 focuses on algorithm design and proofs rather than empirical experimental setup details like hyperparameters or training configurations. |