For many machine learning problems, there are some inputs that are known to be positively (or negatively) related to the output, and in such cases training the model to respect that monotonic relationship can provide regularization, and makes the model more interpretable. However, flexible monotonic functions are computationally challenging to learn beyond a few features. We break through this barrier by learning ensembles of monotonic calibrated interpolated look-up tables (lattices). A key contribution is an automated algorithm for selecting feature subsets for the ensemble base models. We demonstrate that compared to random forests, these ensembles produce similar or better accuracy, while providing guaranteed monotonicity consistent with prior knowledge, smaller model size and faster evaluation.

The paper is heavily based on the previous work on lattice (and monotone lattice) regression, and is thus not self-contained. It is not even explained in the main part of the paper what is the actual function which a lattice represents (two examples are found in the supplementary materials, but I still found the explanation two brief). I think this should definitely be a part of the main paper, otherwise the reader is forced to check the previous work on this topic to understand what kind model is actually being considered. While (7) is convex given calibrators being fixed, it is not clear whether it is convex when jointly optimized over calibration and lattice parameters (I doubt it is). Moreover, (3) seems highly complex and combinatorial optimization criterion.

For many machine learning problems, there are some inputs that are known to be positively (or negatively) related to the output, and in such cases training the model to respect that monotonic relationship can provide regularization, and makes the model more interpretable. However, flexible monotonic functions are computationally challenging to learn beyond a few features. We break through this barrier by learning ensembles of monotonic calibrated interpolated look-up tables (lattices). A key contribution is an automated algorithm for selecting feature subsets for the ensemble base models. We demonstrate that compared to random forests, these ensembles produce similar or better accuracy, while providing guaranteed monotonicity consistent with prior knowledge, smaller model size and faster evaluation. Papers published at the Neural Information Processing Systems Conference.