Authors: Tim Ruhkopf, Aditya Mohan, Difan Deng, Alexander Tornede, Frank Hutter, Marius Lindauer TL;DR: We are augmenting classical algorithm selection with multi-fidelity information, which we make non-myopic through meta-learning – enabling us for the first time to interpret partial learning curves of varying lengths jointly and make good algorithm recommendations at low cost. Why should … Continue reading Experience-Driven Algorithm Selection: Making better and cheaper selection decisions