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Found 10 papers, 1 papers with code
Assessment of Differentially Private Synthetic Data for Utility and Fairness in End-to-End Machine Learning Pipelines for Tabular Data
To the best of our knowledge, our work is the first that: (i) proposes a training and evaluation framework that does not assume that real data is available for testing the utility and fairness of machine learning models trained on synthetic data; (ii) presents the most extensive analysis of synthetic data set generation algorithms in terms of utility and fairness when used for training machine learning models; and (iii) encompasses several different definitions of fairness.
A Mean Field Approach to Empirical Bayes Estimation in High-dimensional Linear Regression
We study empirical Bayes estimation in high-dimensional linear regression.
An Analysis of the Deployment of Models Trained on Private Tabular Synthetic Data: Unexpected Surprises
Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models while respecting the privacy of individual data providers.
A machine learning pipeline for aiding school identification from child trafficking images
Child trafficking in a serious problem around the world.
Variational Inference in high-dimensional linear regression
Using the nascent theory of non-linear large deviations (Chatterjee and Dembo, 2016), we derive sufficient conditions for the leading-order correctness of the naive mean-field approximation to the log-normalizing constant of the posterior distribution.
Reducing bias and increasing utility by federated generative modeling of medical images using a centralized adversary
We show that by using the FELICIA mechanism, a data owner with limited image samples can generate high-quality synthetic images with high utility while neither data owners has to provide access to its data.
Detecting Structured Signals in Ising Models
In this paper, we study the effect of dependence on detecting a class of signals in Ising models, where the signals are present in a structured way.
Probability Statistics Theory Statistics Theory 62G10, 62G20, 62C20
MACE: A Flexible Framework for Membership Privacy Estimation in Generative Models
In this work, we propose the first formal framework for membership privacy estimation in generative models.
privGAN: Protecting GANs from membership inference attacks at low cost
It has been shown that such synthetic data can be used for a variety of downstream tasks such as training classifiers that would otherwise require the original dataset to be shared.
Risks of Using Non-verified Open Data: A case study on using Machine Learning techniques for predicting Pregnancy Outcomes in India
As a result, we can often end up using data that is not representative of the problem we are trying to solve.
