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cs.LG2024
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…
cs.LG2023
Membership Privacy Risks of Sharpness Aware Minimization
Young In Kim, Andrea Agiollo, Pratiksha Agrawal +2
Optimization algorithms that seek flatter minima, such as Sharpness-Aware Minimization (SAM), are credited with improved generalization and robustness to noise. We ask whether such…