134 citations · 330 across the 35 of their papers we have counts for
3 papers · 1 filter
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness
Fanny Yang, Zuowen Wang, Christina Heinze-Deml
This work provides theoretical and empirical evidence that invariance-inducing regularizers can increase predictive accuracy for worst-case spatial transformations (spatial robustn…
Adversarial Training Can Hurt Generalization
Aditi Raghunathan, Sang Michael Xie, Fanny Yang +2
While adversarial training can improve robust accuracy (against an adversary), it sometimes hurts standard accuracy (when there is no adversary). Previous work has studied this tra…
Regularized Learning for Domain Adaptation under Label Shifts
Kamyar Azizzadenesheli, Anqi Liu, Fanny Yang +1
We propose Regularized Learning under Label shifts (RLLS), a principled and a practical domain-adaptation algorithm to correct for shifts in the label distribution between a source…