Distributionally Robust Submodular Maximization

Understand

Submodular functions have applications throughout machine learning, but in many settings, we do not have direct access to the underlying function $f$.

• We focus on stochastic functions that are given as an expectation of functions over a distribution $P$.
• In practice, we often have only a limited set of samples $f_i$ from $P$.
• The standard approach indirectly optimizes $f$ by maximizing the sum of $f_i$.