10 citations · 10 across the 3 of their papers we have counts for
3 papers
Mitigating the Impact of Labeling Errors on Training via Rockafellian Relaxation
Louis L. Chen, Bobbie Chern, Eric Eckstrand +2
Labeling errors in datasets are common, arising in a variety of contexts, such as human labeling, noisy labeling, and weak labeling (i.e., image classification). Although neural ne…
VI-PANN: Harnessing Transfer Learning and Uncertainty-Aware Variational Inference for Improved Generalization in Audio Pattern Recognition
John Fischer, Marko Orescanin, Eric Eckstrand
Transfer learning (TL) is an increasingly popular approach to training deep learning (DL) models that leverages the knowledge gained by training a foundation model on diverse, larg…
Rockafellian Relaxation and Stochastic Optimization under Perturbations
Johannes O. Royset, Louis L. Chen, Eric Eckstrand
In practice, optimization models are often prone to unavoidable inaccuracies due to dubious assumptions and corrupted data. Traditionally, this placed special emphasis on risk-base…