# Joint Continuous and Discrete Model Selection via Submodularity

@article{Bunton2021JointCA, title={Joint Continuous and Discrete Model Selection via Submodularity}, author={Jonathan Bunton and Paulo Tabuada}, journal={ArXiv}, year={2021}, volume={abs/2102.09029} }

. In model selection problems for machine learning, the desire for a well-performing model with meaningful structure is typically expressed through a regularized optimization problem. In many scenar- ios, however, the meaningful structure is speciﬁed in some discrete space, leading to diﬃcult nonconvex optimization problems. In this paper, we connect the model selection problem with structure-promoting regularizers to submodular function minimization with continuous and discrete arguments. In…

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