no code implementations • 22 Jan 2021 • Andreas Bärmann, Oskar Schneider
This is why we present a much more lightweight and accessible approach to Zuckerberg's proof technique, building on ideas from [Extended formulations for convex hulls of some bilinear functions, Discrete Optimization 36, 100569 (2020)].
Optimization and Control 90C57, 52B05, 90C10, 90C27, 90C25
no code implementations • 30 Oct 2018 • Andreas Bärmann, Alexander Martin, Sebastian Pokutta, Oskar Schneider
We also introduce several generalizations, such as the approximate learning of non-linear objective functions, dynamically changing as well as parameterized objectives and the case of suboptimal observed decisions.
no code implementations • ICML 2017 • Andreas Bärmann, Sebastian Pokutta, Oskar Schneider
In this paper, we demonstrate how to learn the objective function of a decision maker while only observing the problem input data and the decision maker’s corresponding decisions over multiple rounds.
