SaDe: Learning Models that Provably Satisfy Domain Constraints
This addresses the need for provably safe models in applications like autonomous driving, offering a novel solution beyond incremental regularization methods.
The paper tackles the problem of ensuring machine learning models satisfy domain constraints (e.g., safety-critical ones) by proposing a framework that guarantees 100% constraint satisfaction on unseen data, using a maximum satisfiability approach combined with gradient descent, and shows it enforces constraints effectively without sacrificing predictive performance compared to regularization-based baselines.
In many real world applications of machine learning, models have to meet certain domain-based requirements that can be expressed as constraints (e.g., safety-critical constraints in autonomous driving systems). Such constraints are often handled by including them in a regularization term, while learning a model. This approach, however, does not guarantee 100% satisfaction of the constraints: it only reduces violations of the constraints on the training set rather than ensuring that the predictions by the model will always adhere to them. In this paper, we present a framework for learning models that provably fulfil the constraints under all circumstances (i.e., also on unseen data). To achieve this, we cast learning as a maximum satisfiability problem, and solve it using a novel SaDe algorithm that combines constraint satisfaction with gradient descent. We compare our method against regularization based baselines on linear models and show that our method is capable of enforcing different types of domain constraints effectively on unseen data, without sacrificing predictive performance.