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.
A Joint Exponential Mechanism For Differentially Private Top-$k$
Authors: Jennifer Gillenwater, Matthew Joseph, Andres Munoz, Monica Ribero Diaz
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 18,216 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,940 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 | Our experiments compare the peeling and joint mechanisms across several real-world datasets using the error metrics from Definition 2.2. |
| Researcher Affiliation | Collaboration | 1Google Research NYC 2UT Austin. |
| Pseudocode | Yes | Algorithm 1 Efficiently sampling JOINT |
| Open Source Code | Yes | All datasets and experiment code are public (Google, 2022). |
| Open Datasets | Yes | We use six datasets: Books (Soumik, 2019) (11,000+ Goodreads books with review counts), Foods (Mc Auley, 2014) (166,000+ Amazon foods with review counts), Games (Tamber, 2016) (5,000+ Steam games with purchase counts), Movies (Harper & Konstan, 2015) (62,000+ Movies with rating counts), News (Fernandes et al., 2015) (40,000+ Mashable articles with share counts), and Tweets (Bin Tareaf, 2017) (52,000+ Tweets with like counts). |
| Dataset Splits | No | The paper does not provide specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) for training, validation, or testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types with speeds, memory amounts, or detailed computer specifications) 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) needed to replicate the experiment. |
| Experiment Setup | Yes | We use k = 5, 15, . . . , 195 with 1-DP instances of JOINT and PNF-PEEL and (1, 10 6)-DP instances of CDP-PEEL. |