Research

Description

We introduce the Disjunctive Refinement Layer (DRL), a novel layer designed to enforce the alignment of generated data with the background knowledge specified in user-defined constraints. DRL is the first method able to automatically make deep learning models inherently compliant with constraints as expressive as quantifier-free linear formulas, which can define non-convex and even disconnected spaces.

Description

We review deep generative modelling approaches for tabular data from the perspective of four types of requirements: utility of the synthetic data, alignment of the synthetic data with domain-specific knowledge, statistical fidelity of the synthetic data distribution compared to the real data distribution, and privacy-preserving capabilities.

We show how deep generative models for tabular data can be constrained such that their generated samples are guaranteed to be compliant with given constraints. This is achieved by automatically parsing the constraints and transforming them into a Constraint Layer seamlessly integrated with the model.

We adapt popular tabular deep generative models into adversarial models and evaluate their effectiveness in generating realistic adversarial examples that conform to domain constraints.

We introduce PiShield, the first package ever allowing for the integration of (propositional or linear) requirements into the neural networks’ topology. PiShield guarantees compliance with these requirements, regardless of input.