47 citations · 262 across the 38 of their papers we have counts for
5 papers · 1 filter
Probable Domain Generalization via Quantile Risk Minimization
Cian Eastwood, Alexander Robey, Shashank Singh +4
Domain generalization (DG) seeks predictors which perform well on unseen test distributions by leveraging data drawn from multiple related training distributions or domains. To ach…
Toward Certified Robustness Against Real-World Distribution Shifts
Haoze Wu, Teruhiro Tagomori, Alexander Robey +6
We consider the problem of certifying the robustness of deep neural networks against real-world distribution shifts. To do so, we bridge the gap between hand-crafted specifications…
Chordal Sparsity for Lipschitz Constant Estimation of Deep Neural Networks
Anton Xue, Lars Lindemann, Alexander Robey +3
Lipschitz constants of neural networks allow for guarantees of robustness in image classification, safety in controller design, and generalizability beyond the training data. As ca…
Do Deep Networks Transfer Invariances Across Classes?
Allan Zhou, Fahim Tajwar, Alexander Robey +4
To generalize well, classifiers must learn to be invariant to nuisance transformations that do not alter an input's class. Many problems have "class-agnostic" nuisance transformati…
Probabilistically Robust Learning: Balancing Average- and Worst-case Performance
Alexander Robey, Luiz F. O. Chamon, George J. Pappas +1
Many of the successes of machine learning are based on minimizing an averaged loss function. However, it is well-known that this paradigm suffers from robustness issues that hinder…