Copulas
SDGym
| Copulas | SDGym | |
|---|---|---|
| 1 | 1 | |
| 556 | 263 | |
| 0.4% | 1.5% | |
| 8.2 | 8.6 | |
| 19 days ago | 4 days ago | |
| Python | Python | |
| GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Copulas
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[D] Has anyone used "copulas" before?
nice Python library for modeling with copulas that I've worked with: https://github.com/sdv-dev/Copulas
SDGym
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[D] Synthetic data generation techniques for data privacy
I would suggest starting with "differentially private synthetic data generation". These methods utilize differential privacy and mostly protect against membership inference attacks, are very popular in the ML/DL community. I would also suggest reading up on privacy preserving ML methods in general and adversarial attacks against them (membership inference, inversion, reconstruction, property inference), but if you're keen on reading some code, check out sd-gym: https://github.com/sdv-dev/SDGym. The authors have collected implementations for a lot of PPSDG methods. Also I strongly suggest reading McMahan's 2016 paper: https://arxiv.org/abs/1607.00133.
What are some alternatives?
CTGAN - Conditional GAN for generating synthetic tabular data.
Mimesis - Mimesis is a robust data generator for Python that can produce a wide range of fake data in multiple languages.
Made-With-ML - Learn how to design, develop, deploy and iterate on production-grade ML applications.
DPL - [NeurIPS 2023] Multi-fidelity hyperparameter optimization with deep power laws that achieves state-of-the-art results across diverse benchmarks.
SDV - Synthetic data generation for tabular data
gretel-synthetics - Synthetic data generators for structured and unstructured text, featuring differentially private learning.
FAST-RIR - This is the official implementation of our neural-network-based fast diffuse room impulse response generator (FAST-RIR) for generating room impulse responses (RIRs) for a given acoustic environment.
gretel-python-client - The Gretel Python Client allows you to interact with the Gretel REST API.
DeepEcho - Synthetic Data Generation for mixed-type, multivariate time series.
ydata-synthetic - Synthetic data generators for tabular and time-series data
Main - Main folder. Material related to my books on synthetic data and generative AI. Also contains documents blending components from several folders, or covering topics spanning across multiple folders..