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DeepSaDe: Learning Neural Networks that Guarantee Domain Constraint Satisfaction
In this paper, we present an approach to train neural networks which can enforce a wide variety of constraints and guarantee that the constraint is satisfied by all possible predictions.
Automatic Generation of Product Concepts from Positive Examples, with an Application to Music Streaming
In this work, we represent product concepts using database queries and tackle two learning problems.
SaDe: Learning Models that Provably Satisfy Domain Constraints
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).
Feature Interactions in XGBoost
Further, the improvement in the model structure can also lead to better interpretability.
