no code implementations • EMNLP (BlackboxNLP) 2021 • Zhouhang Xie, Jonathan Brophy, Adam Noack, Wencong You, Kalyani Asthana, Carter Perkins, Sabrina Reis, Zayd Hammoudeh, Daniel Lowd, Sameer Singh
Adversarial attacks curated against NLP models are increasingly becoming practical threats.
no code implementations • 28 Oct 2023 • Wencong You, Zayd Hammoudeh, Daniel Lowd
Backdoor attacks manipulate model predictions by inserting innocuous triggers into training and test data.
2 code implementations • 22 Feb 2023 • Zayd Hammoudeh, Daniel Lowd
Sparse or $\ell_0$ adversarial attacks arbitrarily perturb an unknown subset of the features.
1 code implementation • 9 Dec 2022 • Zayd Hammoudeh, Daniel Lowd
Good models require good training data.
1 code implementation • 29 Aug 2022 • Zayd Hammoudeh, Daniel Lowd
We also show that the assumptions made by existing state-of-the-art certified classifiers are often overly pessimistic.
1 code implementation • 30 Apr 2022 • Jonathan Brophy, Zayd Hammoudeh, Daniel Lowd
In the pursuit of better understanding GBDT predictions and generally improving these models, we adapt recent and popular influence-estimation methods designed for deep learning models to GBDTs.
1 code implementation • 25 Jan 2022 • Zayd Hammoudeh, Daniel Lowd
This work proposes the task of target identification, which determines whether a specific test instance is the target of a training-set attack.
1 code implementation • NeurIPS 2020 • Zayd Hammoudeh, Daniel Lowd
A common simplifying assumption is that the positive data is representative of the target positive class.