• Corpus ID: 231951679

Joint Continuous and Discrete Model Selection via Submodularity

  title={Joint Continuous and Discrete Model Selection via Submodularity},
  author={Jonathan Bunton and Paulo Tabuada},
. 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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