Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification

@article{Monteiro2022MonotonicityRI,
  title={Monotonicity Regularization: Improved Penalties and Novel Applications to Disentangled Representation Learning and Robust Classification},
  author={Joao Monteiro and Mohamed Osama Ahmed and Hossein Hajimirsadeghi and Greg Mori},
  journal={ArXiv},
  year={2022},
  volume={abs/2205.08247}
}
We study settings where gradient penalties are used alongside risk minimization with the goal of ob-taining predictors satisfying different notions of monotonicity. Specifically, we present two sets of contributions. In the first part of the paper, we show that different choices of penalties define the regions of the input space where the property is observed. As such, previous methods result in models that are monotonic only in a small volume of the input space. We thus propose an approach that… 

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