17 citations · 18 across the 3 of their papers we have counts for
8 papers
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…
Adversarial Robustness with Semi-Infinite Constrained Learning
Alexander Robey, Luiz F. O. Chamon, George J. Pappas +2
Despite strong performance in numerous applications, the fragility of deep learning to input perturbations has raised serious questions about its use in safety-critical domains. Wh…
Learning Robust Hybrid Control Barrier Functions for Uncertain Systems
Alexander Robey, Lars Lindemann, Stephen Tu +1
The need for robust control laws is especially important in safety-critical applications. We propose robust hybrid control barrier functions as a means to synthesize control laws t…
Learning Hybrid Control Barrier Functions from Data
Lars Lindemann, Haimin Hu, Alexander Robey +4
Motivated by the lack of systematic tools to obtain safe control laws for hybrid systems, we propose an optimization-based framework for learning certifiably safe control laws from…
Model-Based Robust Deep Learning: Generalizing to Natural, Out-of-Distribution Data
Alexander Robey, Hamed Hassani, George J. Pappas
While deep learning has resulted in major breakthroughs in many application domains, the frameworks commonly used in deep learning remain fragile to artificially-crafted and imperc…
Learning Control Barrier Functions from Expert Demonstrations
Alexander Robey, Haimin Hu, Lars Lindemann +4
Inspired by the success of imitation and inverse reinforcement learning in replicating expert behavior through optimal control, we propose a learning based approach to safe control…