Preference learning along multiple criteria: A game-theoretic perspective
Kush Bhatia, Ashwin Pananjady, Peter Bartlett, Anca Dragan, Martin J. Wainwright
From a theoretical standpoint, we show that the Blackwell winner of a multi-criteria problem instance can be computed as the solution to a convex optimization problem. Furthermore, given random samples of pairwise comparisons, we show that a simple, "plug-in" estimator achieves (near-)optimal minimax sample complexity. Finally, we showcase the practical utility of our framework in a user study on autonomous driving, where we find that the Blackwell winner outperforms the von Neumann winner for the overall preferences.
