Learning Sparse Classifiers: Continuous and Mixed Integer Optimization Perspectives

17 Jan 2020Antoine DedieuHussein HazimehRahul Mazumder

We consider a discrete optimization based approach for learning sparse classifiers, where the outcome depends upon a linear combination of a small subset of features. Recent work has shown that mixed integer programming (MIP) can be used to solve (to optimality) $\ell_0$-regularized problems at scales much larger than what was conventionally considered possible in the statistics and machine learning communities... (read more)

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