Predictive Zonotope Reduction: Precise Runtime Monitoring under Uncertainty

What the math gives to ML

The paper treats uncertainty-representation compression as a sequential decision problem rather than applying one fixed reduction rule everywhere. Its transferable asset is the zonotope representation, whose generator-wise absolute sums provide cheap coordinatewise hull bounds, combined with finite-horizon lookahead over candidate reductions. This can support certified neural-network inference by dynamically compressing activation uncertainty after affine or nonlinear layers while minimizing predicted downstream over-approximation error. The practical first target is a robust MLP, where several inexpensive generator-pruning rules are selected per layer and input instead of using one global reducer.