Improving Offline Contextual Bandits with Distributional Robustness

Understand

This paper extends the Distributionally Robust Optimization (DRO) approach for offline contextual bandits.

• Specifically, we leverage this framework to introduce a convex reformulation of the Counterfactual Risk Minimization principle.
• Besides relying on convex programs, our approach is compatible with stochastic optimization, and can therefore be readily adapted tothe large data regime.
• Our approach relies on the construction of asymptotic confidence intervals for offline contextual bandits through the DRO framework.