Generative Adversarial Regression (GAR): Learning Conditional Risk Scenarios

ArXi:2603.08553v1 Announce Type: cross We propose Generative Adversarial Regression (GAR), a framework for learning conditional risk scenarios through generators aligned with downstream risk objectives. GAR builds on a regression characterization of conditional risk for elicitable functionals, including quantiles, expectiles, and jointly elicitable pairs. We extend this principle from point prediction to generative modeling by