Differentially Private Maximal Information Coefficients
Authors: John Lazarsfeld, Aaron Johnson, Emmanuel Adeniran
ICML 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We perform experiments on a variety of real and synthetic datasets. |
| Researcher Affiliation | Collaboration | 1Department of Computer Science, Yale University 2U.S. Naval Research Laboratory. Correspondence to: John Lazarsfeld <john.lazarsfeld@yale.edu>. |
| Pseudocode | No | The paper describes its mechanisms (e.g., Mechanism 1, Mechanism 2, Mechanism 3) in prose, and refers to the OPTIMIZEAXIS routine, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The code and data used to obtain our experimental results can be accessed at https://github.com/jlazarsfeld/dp-mic, and more implementation details are given in Appendix E.1. |
| Open Datasets | Yes | We used synthetic data (following the methodology of Reshef et al. (2011; 2016))...The Spellman data (Spellman et al., 1998; Reshef et al., 2011)...Finally, the Baseball dataset (Reshef et al., 2011; Prospectus, Accessed March 2020)...The code and data used to obtain our experimental results can be accessed at https://github.com/jlazarsfeld/dp-mic |
| Dataset Splits | No | The paper describes parameter tuning on synthetic data and analyzes bias/variance, which functionally serves as validation. However, it does not explicitly provide details about 'training/test/validation dataset splits' using standard terminology or specific proportions for model training. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'the MINEPY library implementation from Albanese et al. (2018)' but does not provide a specific version number for MINEPY or any other software dependencies. |
| Experiment Setup | Yes | We set its parameters to B(n) = n0.6 and c = 15...We considered sample sizes of n {25, 250, 500, 1000, 5000, 10000}, ϵ {0.1, 1.0}, and various values of B between 4 and 150...we fixed c = 5 for the MICr-Lap mechanism...and for the MICr-Geom mechanism we considered c {1, 2}... |