1 citations · 2 across the 5 of their papers we have counts for
6 papers
Multilingual Unlearning in LLMs: Transfer, Dynamics, and Reversibility
Chaoyi Xiang, Olga Ohrimenko, Benjamin I. P. Rubinstein +1
Large language models (LLMs) can memorize sensitive facts, motivating unlearning methods that remove targeted knowledge without costly retraining. However, unlearning research rema…
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…
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…
Combining Classical and Probabilistic Independence Reasoning to Verify the Security of Oblivious Algorithms (Extended Version)
Pengbo Yan, Toby Murray, Olga Ohrimenko +2
We consider the problem of how to verify the security of probabilistic oblivious algorithms formally and systematically. Unfortunately, prior program logics fail to support a numbe…
RS-Reg: Probabilistic and Robust Certified Regression Through Randomized Smoothing
Aref Miri Rekavandi, Olga Ohrimenko, Benjamin I. P. Rubinstein
Randomized smoothing has shown promising certified robustness against adversaries in classification tasks. Despite such success with only zeroth-order access to base models, random…
Elephants Do Not Forget: Differential Privacy with State Continuity for Privacy Budget
Jiankai Jin, Chitchanok Chuengsatiansup, Toby Murray +3
Current implementations of differentially-private (DP) systems either lack support to track the global privacy budget consumed on a dataset, or fail to faithfully maintain the stat…