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..
Collect at Once, Use Effectively: Making Non-interactive Locally Private Learning Possible
Authors: Kai Zheng, Wenlong Mou, Liwei Wang
ICML 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | In this paper, we propose several efο¬cient algorithms for learning and estimation problems under non-interactive LDP model, with good theoretical guarantees. |
| Researcher Affiliation | Academia | 1Key Laboratory of Machine Perception, MOE, School of EECS, Peking University, Beijing, China. Correspondence to: Kai Zheng <EMAIL>, Wenlong Mou <EMAIL>, Liwei Wang <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Basic Private Vector mechanism ... Algorithm 2 LDP β1 Constrained Mean Estimation ... Algorithm 3 LDP β1 Constrained Linear Regression ... Algorithm 4 LDP kernel mechanism ... Algorithm 5 LDP SGLD Mechanism Collection ... Algorithm 6 LDP SGLD Mechanism Learning |
| Open Source Code | No | The paper does not include any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper is theoretical and does not conduct experiments on a specific publicly available dataset. It defines problem settings with data properties (e.g., 'distribution D supported on B(0, 1)') but does not provide access information for empirical data. |
| Dataset Splits | No | The paper is theoretical and does not present empirical experiments, therefore it does not provide training/test/validation dataset splits. |
| Hardware Specification | No | The paper is theoretical and does not describe any experimental setup or specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not describe any specific software dependencies with version numbers for replication. |
| Experiment Setup | No | The paper is theoretical and does not include details about an experimental setup such as hyperparameters or training configurations. |