We exploit the convexity property of the barrier function to formulate the optimal control synthesis problem asalinear program.

Certified adversarial robustness of large-scale deep networks has progressed substantially after the introduction of randomized smoothing.

Non-convex optimization is a difficult and crucial task.

Randomized smoothing is a powerful framework for making models provably robust against small changes to their inputs - by guaranteeing robustness of the majority vote when randomly adding noise before classification.

However, these results can be too general to be applicable to natural data distributions. Indeed, humans are quite robust for tasks involving vision.

To remedy this, we introduce temporal certificate sets, a general notion for capturing the intrinsic temporal understanding length associated with a broad range of video understanding tasks & datasets.