45 citations · 77 across the 8 of their papers we have counts for
8 papers
On Using Certified Training towards Empirical Robustness
Alessandro De Palma, Serge Durand, Zakaria Chihani +2
Adversarial training is arguably the most popular way to provide empirical robustness against specific adversarial examples. While variants based on multi-step attacks incur signif…
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…
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…
Improved Branch and Bound for Neural Network Verification via Lagrangian Decomposition
Alessandro De Palma, Rudy Bunel, Alban Desmaison +4
We improve the scalability of Branch and Bound (BaB) algorithms for formally proving input-output properties of neural networks. First, we propose novel bounding algorithms based o…
Lagrangian Decomposition for Neural Network Verification
Rudy Bunel, Alessandro De Palma, Alban Desmaison +4
A fundamental component of neural network verification is the computation of bounds on the values their outputs can take. Previous methods have either used off-the-shelf solvers, d…
Sampling Acquisition Functions for Batch Bayesian Optimization
Alessandro De Palma, Celestine Mendler-Dünner, Thomas Parnell +2
We present Acquisition Thompson Sampling (ATS), a novel technique for batch Bayesian Optimization (BO) based on the idea of sampling multiple acquisition functions from a stochasti…