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.
Renyi Differential Privacy Mechanisms for Posterior Sampling
Authors: Joseph Geumlek, Shuang Song, Kamalika Chaudhuri
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,617 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,441 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 | In this section, we present the experimental results for our proposed algorithms for both exponential family and GLMs. Our experimental design focuses on two goals first, analyzing the relationship between λ and ϵ in our privacy guarantees and second, exploring the privacy-utility trade-off of our proposed methods in relation to existing methods. |
| Researcher Affiliation | Academia | Joseph Geumlek University of California, San Diego EMAIL Shuang Song University of California, San Diego EMAIL Kamalika Chaudhuri University of California, San Diego EMAIL |
| Pseudocode | Yes | Algorithm 1 Direct Posterior... Algorithm 2 Diffused Posterior... Algorithm 3 Concentrated Posterior... Algorithm 4 Concentrated Posterior... Algorithm 5 Diffuse Posterior |
| Open Source Code | No | The paper does not provide any explicit statements about releasing source code for the methodology or links to a code repository. |
| Open Datasets | Yes | We conduct Bayesian logistic regression on three real datasets: Abalone, Adult and MNIST. |
| Dataset Splits | No | The paper states '1/3 of the each dataset is used for testing, and the rest for training.' and provides specific training and test sample counts, but does not explicitly mention a separate validation split or cross-validation strategy. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details (e.g., library or solver names with version numbers). |
| Experiment Setup | Yes | For all algorithms, we use an original Gaussian prior with β = 10-3. The posterior sampling is done using slice sampling with 1000 burn-in samples. ... 500 iterations of binary search were used to select r and m when needed. ... For privacy parameters, we set λ = 1, 10, 100 and ϵ {e-5, e-4, . . . , e3}. |