In this talk, we explain how two-stage worst-case regret minimization problems can be reformulated as two-stage robust optimization models. This allows us to employ both approximate and exact solution methods that are available in the recent literature to fficiently identify good solutions for these hard problems. In particular, our numerical experiments indicate that affine decision rules are particularly effective at identifying good conservative solutions for three different types of decision problems: a multi-item newsvendor problem, a lot-sizing problem, and a production-transportation problem.