Poster
Multidimensional Shape Constraints
Maya Gupta · Erez Louidor · Oleksandr Mangylov · Nobu Morioka · Taman Narayan · Sen Zhao
Keywords: [ Safety ] [ Other ] [ Accountability, Transparency and Interpretability ] [ Supervised Learning ]
We propose new multi-input shape constraints across four intuitive categories: complements, diminishers, dominance, and unimodality constraints. We show these shape constraints can be checked and even enforced when training machine-learned models for linear models, generalized additive models, and the nonlinear function class of multi-layer lattice models. Toy examples and real-world experiments illustrate how the different shape constraints can be used to increase interpretability and better regularize machine-learned models.