Konstantin Hess

Representation Learning for Exact Preimages

This work introduces TRIO (tractable representations for preimage learning and inverse optimization), a framework for learning representations that make these objectives compatible by construction, and proves a universal approximation theorem which shows that TRIO can approximate any continuous forward map and its enti...