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Found 6 papers, 2 papers with code
A Novel ML-driven Test Case Selection Approach for Enhancing the Performance of Grammatical Evolution
The quality of the solutions is tested and compared against the conventional training method to measure the coverage of training data selected using DBS, i. e., how well the subset matches the statistical properties of the entire dataset.
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.
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.
Interpretable Network Propagation with Application to Expanding the Repertoire of Human Proteins that Interact with SARS-CoV-2
Despite the popularity of this approach, little attention has been paid to the question of provenance tracing in this context, e. g., determining how much any experimental observation in the input contributes to the score of every prediction.
Learning task structure via sparsity grouped multitask learning
We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.
