Poster

Multidimensional Shape Constraints

Maya Gupta · Erez Louidor · Oleksandr Mangylov · Nobu Morioka · Taman Narayan · Sen Zhao

Keywords: [ Supervised Learning ] [ Accountability, Transparency and Interpretability ] [ Other ] [ Safety ]

Abstract:

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.

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