8 papers
Correcting Class Imbalance in Prior-Data Fitted Networks for Tabular Classification
Samuel McDowell, Nathan Stromberg, Lalitha Sankar
Prior-data fitted networks (PFNs) have achieved exceptional performance on tabular classification tasks. However, like other classifiers, their performance can suffer under the eff…
Thumb on the Scale: Optimal Loss Weighting in Last Layer Retraining
Nathan Stromberg, Christos Thrampoulidis, Lalitha Sankar
While machine learning models become more capable in discriminative tasks at scale, their ability to overcome biases introduced by training data has come under increasing scrutiny.…
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…
Label Noise Robustness for Domain-Agnostic Fair Corrections via Nearest Neighbors Label Spreading
Nathan Stromberg, Rohan Ayyagari, Sanmi Koyejo +2
Last-layer retraining methods have emerged as an efficient framework for correcting existing base models. Within this framework, several methods have been proposed to deal with cor…
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…
Smoothly Giving up: Robustness for Simple Models
Tyler Sypherd, Nathan Stromberg, Richard Nock +2
There is a growing need for models that are interpretable and have reduced energy and computational cost (e.g., in health care analytics and federated learning). Examples of algori…