In this paper, we examine a data-driven optimization approach to making optimal decisions as evaluated by trained random forest, where these can be constrained an arbitrary polyhedral set. We model problem mixed-integer linear program. show solved optimality efficiently using pareto-optimal Benders cuts for ensembles containing modest number of trees. consider forest approximation that consists...

Neural networks can learn complex, non-convex functions, and it is challenging to guarantee their correct behavior in safety-critical contexts. Many approaches exist find failures (e.g., adversarial examples), but these cannot the absence of failures. Verification algorithms address this need provide formal guarantees about a neural network by answering “yes or no” questions. For example, they ...

Abstract According to the objective Bayesian approach inductive logic, premisses inductively entail a conclusion just when every probability function with maximal entropy, from all those that satisfy premisses, satisfies conclusion. When and are constraints on probabilities of sentences first-order predicate language, however, it is by no means obvious how determine these entropy functions. Thi...

in this paper, multi-objective uniform-diversity genetic algorithm (muga) with a diversity preserving mechanism called the ε-elimination algorithm is used for pareto optimization of 5-degree of freedom vehicle vibration model considering the five conflicting functions simultaneously. the important conflicting objective functions that have been considered in this work are, namely, vertical accel...