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.
Generalized Linear Bandits with Local Differential Privacy
Authors: Yuxuan Han, Zhipeng Liang, Yang Wang, Jiheng Zhang
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 16,920 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,324 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we conduct experiments with both simulation and real-world datasets to demonstrate the consistently superb performance of our algorithms under LDP constraints with reasonably small parameters (ε, δ) to ensure strong privacy protection. |
| Researcher Affiliation | Academia | The Hong Kong University of Science and Technology |
| Pseudocode | Yes | Algorithm 1: LDP Single-parameter Contextual Bandit and Algorithm 2: LDP Multi-parameter Contextual Bandit |
| Open Source Code | Yes | The source code to reproduce all the results is available at the Git Hub repo liangzp/LDP-Bandit. |
| Open Datasets | Yes | On-Line Auto Lending dataset CRPM-12-001 provided by Columbia University https://www8.gsb. columbia.edu/cprm/research/datasets, and has been used in the study of contextual bandits by [26, 11]. |
| Dataset Splits | No | The paper does not explicitly provide specific percentages or counts for training, validation, or test splits. It mentions 'synthetic datasets' and 'real-world datasets' without specifying how they were partitioned. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, memory, or cloud instance types used for running its experiments. It only mentions 'simulation' and 'real-world datasets' without hardware context. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. It refers to 'scikit-learn' through citation [25], but no version is specified. It also mentions 'LDP-UCB' and 'LDP-GLOC' as methods being compared, but these are not software dependencies in the sense of libraries with versions. |
| Experiment Setup | Yes | We evaluate all the four methods on two different privacy levels ε = 0.5 and 1 in synthetic datasets, which are industry standards. For the sake of comparison, the learning step parameter for LDP-GLOC and LDP-SGD are tuned in the same way. |