565 citations · 612 across the 5 of their papers we have counts for
5 papers
Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models
Aarohi Srivastava, Abhinav Rastogi, Abhishek Rao +448
Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabil…
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…