no code implementations • 21 Dec 2023 • Krishn Kumar Gupt, Meghana Kshirsagar, Douglas Mota Dias, Joseph P. Sullivan, Conor Ryan
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.
no code implementations • 30 Oct 2023 • Mayana Pereira, Meghana Kshirsagar, Sumit Mukherjee, Rahul Dodhia, Juan Lavista Ferres, Rafael de Sousa
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.
no code implementations • 15 Jun 2021 • Mayana Pereira, Meghana Kshirsagar, Sumit Mukherjee, Rahul Dodhia, Juan Lavista Ferres
Diferentially private (DP) synthetic datasets are a powerful approach for training machine learning models while respecting the privacy of individual data providers.
1 code implementation • 2 Jun 2020 • Jeffrey N. Law, Kyle Akers, Nure Tasnina, Catherine M. Della Santina, Shay Deutsch, Meghana Kshirsagar, Judith Klein-Seetharaman, Mark Crovella, Padmavathy Rajagopalan, Simon Kasif, T. M. Murali
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.
1 code implementation • 13 May 2017 • Meghana Kshirsagar, Eunho Yang, Aurélie C. Lozano
We further demonstrate that our proposed method recovers groups and the sparsity patterns in the task parameters accurately by extensive experiments.