1 citations · 2 across the 7 of their papers we have counts for
8 papers
CORAL: Disentangling Latent Representations in Long-Tailed Diffusion
Esther Rodriguez, Monica Welfert, Samuel McDowell +3
Diffusion models have achieved impressive performance in generating high-quality and diverse synthetic data. However, their success typically assumes a class-balanced training dist…
Lower Bounds on the MMSE of Adversarially Inferring Sensitive Features
Monica Welfert, Nathan Stromberg, Mario Diaz +1
We propose an adversarial evaluation framework for sensitive feature inference based on minimum mean-squared error (MMSE) estimation with a finite sample size and linear predictive…
Theoretical Guarantees of Data Augmented Last Layer Retraining Methods
Monica Welfert, Nathan Stromberg, Lalitha Sankar
Ensuring fair predictions across many distinct subpopulations in the training data can be prohibitive for large models. Recently, simple linear last layer retraining strategies, in…
Robustness to Subpopulation Shift with Domain Label Noise via Regularized Annotation of Domains
Nathan Stromberg, Rohan Ayyagari, Monica Welfert +3
Existing methods for last layer retraining that aim to optimize worst-group accuracy (WGA) rely heavily on well-annotated groups in the training data. We show, both in theory and p…
Addressing GAN Training Instabilities via Tunable Classification Losses
Monica Welfert, Gowtham R. Kurri, Kyle Otstot +1
Generative adversarial networks (GANs), modeled as a zero-sum game between a generator (G) and a discriminator (D), allow generating synthetic data with formal guarantees. Noting t…
-GANs: Addressing GAN Training Instabilities via Dual Objectives
Monica Welfert, Kyle Otstot, Gowtham R. Kurri +1
In an effort to address the training instabilities of GANs, we introduce a class of dual-objective GANs with different value functions (objectives) for the generator (G) and discri…
-GAN: Convergence and Estimation Guarantees
Gowtham R. Kurri, Monica Welfert, Tyler Sypherd +1
We prove a two-way correspondence between the min-max optimization of general CPE loss function GANs and the minimization of associated -divergences. We then focus on -GAN, d…
Generating Fair Universal Representations using Adversarial Models
Peter Kairouz, Jiachun Liao, Chong Huang +3
We present a data-driven framework for learning fair universal representations (FUR) that guarantee statistical fairness for any learning task that may not be known a priori. Our f…