# Hard Shape-Constrained Kernel Machines

@article{AubinFrankowski2020HardSK, title={Hard Shape-Constrained Kernel Machines}, author={Pierre-Cyril Aubin-Frankowski and Zolt{\'a}n Szab{\'o}}, journal={ArXiv}, year={2020}, volume={abs/2005.12636} }

Shape constraints (such as non-negativity, monotonicity, convexity) play a central role in a large number of applications, as they usually improve performance for small sample size and help interpretability. However enforcing these shape requirements in a hard fashion is an extremely challenging problem. Classically, this task is tackled (i) in a soft way (without out-of-sample guarantees), (ii) by specialized transformation of the variables on a case-by-case basis, or (iii) by using highly…

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