280 citations · 1k across the 17 of their papers we have counts for
17 papers
Operationalizing Contextual Integrity in Privacy-Conscious Assistants
Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi +10
Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramati…
Verified Neural Compressed Sensing
Rudy Bunel, Krishnamurthy Dvijotham, M. Pawan Kumar +2
We develop the first (to the best of our knowledge) provably correct neural networks for a precise computational task, with the proof of correctness generated by an automated verif…
Unlocking Accuracy and Fairness in Differentially Private Image Classification
Leonard Berrada, Soham De, Judy Hanwen Shen +6
Privacy-preserving machine learning aims to train models on private data without leaking sensitive information. Differential privacy (DP) is considered the gold standard framework…
Expressive Losses for Verified Robustness via Convex Combinations
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham +3
In order to train networks for verified adversarial robustness, it is common to over-approximate the worst-case loss over perturbation regions, resulting in networks that attain ve…
Differentially Private Diffusion Models Generate Useful Synthetic Images
Sahra Ghalebikesabi, Leonard Berrada, Sven Gowal +7
The ability to generate privacy-preserving synthetic versions of sensitive image datasets could unlock numerous ML applications currently constrained by data availability. Due to t…
IBP Regularization for Verified Adversarial Robustness via Branch-and-Bound
Alessandro De Palma, Rudy Bunel, Krishnamurthy Dvijotham +2
Recent works have tried to increase the verifiability of adversarially trained networks by running the attacks over domains larger than the original perturbations and adding variou…