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.
Learning-augmented private algorithms for multiple quantile release
Authors: Mikhail Khodak, Kareem Amin, Travis Dick, Sergei Vassilvitskii
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 41,063 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 6,259 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 | We conclude with experiments on challenging tasks demonstrating that learning predictions across one or more instances can lead to large error reductions while preserving privacy. |
| Researcher Affiliation | Collaboration | 1Carnegie Mellon University; work done in part as an intern at Google Research New York. 2Google Research New York. |
| Pseudocode | Yes | Algorithm 2: Approximate Quantiles with predictions |
| Open Source Code | Yes | Code to reproduce our results is available at https://github.com/mkhodak/private-quantiles. |
| Open Datasets | Yes | We evaluate this approach... on Adult (Kohavi, 1996) and Goodreads (Wan & Mc Auley, 2018)... |
| Dataset Splits | Yes | Adult tests the D D1 case, with its train set the public dataset and a hundred samples from test as private. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as exact GPU or CPU models, memory configurations, or specific cloud computing instance types. |
| Software Dependencies | No | The paper mentions various software components and packages used, such as COCOB (with a GitHub link), textstat (with a GitHub link), NLTK, and an adaptation of DP-FTRL from Google Research. However, it does not provide specific version numbers for any of these dependencies or the general programming environment (e.g., Python version, PyTorch/TensorFlow versions). |
| Experiment Setup | Yes | We use the following reasonable guesses for locations ν, scales σ, and quantile ranges ra, bs for these distributions: age: ν 40, σ 5, a 10, b 120; hours: ν 40, σ 2, a 0, b 168; rating: ν 2.5, σ 0.5, a 0, b 5; page count: ν 200, σ 25, a 0, b 1000 |