6 citations · 10 across the 4 of their papers we have counts for
5 papers
AdaptDel: Adaptable Deletion Rate Randomized Smoothing for Certified Robustness
Zhuoqun Huang, Neil G. Marchant, Olga Ohrimenko +1
We consider the problem of certified robustness for sequence classification against edit distance perturbations. Naturally occurring inputs of varying lengths (e.g., sentences in n…
How to Enhance Downstream Adversarial Robustness (almost) without Touching the Pre-Trained Foundation Model?
Meiqi Liu, Zhuoqun Huang, Yue Xing
With the rise of powerful foundation models, a pre-training-fine-tuning paradigm becomes increasingly popular these days: A foundation model is pre-trained using a huge amount of d…
CERT-ED: Certifiably Robust Text Classification for Edit Distance
Zhuoqun Huang, Neil G Marchant, Olga Ohrimenko +1
With the growing integration of AI in daily life, ensuring the robustness of systems to inference-time attacks is crucial. Among the approaches for certifying robustness to such ad…
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
Zhuoqun Huang, Neil G. Marchant, Keane Lucas +3
Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused…
A Unified Evaluation of Two-Candidate Ballot-Polling Election Auditing Methods
Zhuoqun Huang, Ronald L. Rivest, Philip B. Stark +2
Counting votes is complex and error-prone. Several statistical methods have been developed to assess election accuracy by manually inspecting randomly selected physical ballots. Tw…