45 citations · 46 across the 3 of their papers we have counts for
4 papers
Adaptive Machine Unlearning
Varun Gupta, Christopher Jung, Seth Neel +3
Data deletion algorithms aim to remove the influence of deleted data points from trained models at a cheaper computational cost than fully retraining those models. However, for seq…
GABO: Graph Augmentations with Bi-level Optimization
Heejung W. Chung, Avoy Datta, Chris Waites
Data augmentation refers to a wide range of techniques for improving model generalization by augmenting training examples. Oftentimes such methods require domain knowledge about th…
Differentially Private Normalizing Flows for Privacy-Preserving Density Estimation
Chris Waites, Rachel Cummings
Normalizing flow models have risen as a popular solution to the problem of density estimation, enabling high-quality synthetic data generation as well as exact probability density…
Differentially Private Synthetic Mixed-Type Data Generation For Unsupervised Learning
Uthaipon Tantipongpipat, Chris Waites, Digvijay Boob +2
We introduce the DP-auto-GAN framework for synthetic data generation, which combines the low dimensional representation of autoencoders with the flexibility of Generative Adversari…