2.3k citations · 3.9k across the 40 of their papers we have counts for
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Certified Robustness to Adversarial Word Substitutions
Robin Jia, Aditi Raghunathan, Kerem Göksel +1
State-of-the-art NLP models can often be fooled by adversaries that apply seemingly innocuous label-preserving transformations (e.g., paraphrasing) to input text. The number of pos…
Maximum Weighted Loss Discrepancy
Fereshte Khani, Aditi Raghunathan, Percy Liang
Though machine learning algorithms excel at minimizing the average loss over a population, this might lead to large discrepancies between the losses across groups within the popula…
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…
Unlabeled Data Improves Adversarial Robustness
Yair Carmon, Aditi Raghunathan, Ludwig Schmidt +2
We demonstrate, theoretically and empirically, that adversarial robustness can significantly benefit from semisupervised learning. Theoretically, we revisit the simple Gaussian mod…