  We consider robust empirical risk minimization (ERM), where model parameters
are chosen to minimize the worst-case empirical loss when each data point
varies over a given convex uncertainty set. In some simple cases, such problems
can be expressed in an analytical form. In general the problem can be made
tractable via dualization, which turns a min-max problem into a min-min
problem. Dualization requires expertise and is tedious and error-prone. We
demonstrate how CVXPY can be used to automate this dualization procedure in a
user-friendly manner. Our framework allows practitioners to specify and solve
robust ERM problems with a general class of convex losses, capturing many
standard regression and classification problems. Users can easily specify any
complex uncertainty set that is representable via disciplined convex
programming (DCP) constraints.
