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Found 5 papers, 1 papers with code
Tactics on Refining Decision Boundary for Improving Certification-based Robust Training
In verification-based robust training, existing methods utilize relaxation based methods to bound the worst case performance of neural networks given certain perturbation.
Efficient Certification for Probabilistic Robustness
Recent developments on the robustness of neural networks have primarily emphasized the notion of worst-case adversarial robustness in both verification and robust training.
Robust Online Control with Model Misspecification
We study online control of an unknown nonlinear dynamical system that is approximated by a time-invariant linear system with model misspecification.
Robust Deep Reinforcement Learning through Adversarial Loss
To address this issue, we propose RADIAL-RL, a principled framework to train reinforcement learning agents with improved robustness against $l_p$-norm bounded adversarial attacks.
Convex Parameterizations and Fidelity Bounds for Nonlinear Identification and Reduced-Order Modelling
Model instability and poor prediction of long-term behavior are common problems when modeling dynamical systems using nonlinear "black-box" techniques.
