Bärmann A, Pokutta S, Schneider O (2017)
Publication Language: English
Publication Type: Conference contribution
Publication year: 2017
Publisher: PMLR
Series: Proceedings of Machine Learning Research
City/Town: International Convention Centre, Sydney, Australia
Book Volume: 70
Pages Range: 400--410
Conference Proceedings Title: Proceedings of the 34th International Conference on Machine Learning (ICML)
URI: http://proceedings.mlr.press/v70/barmann17a.html
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. Our approach is based on online learning techniques and works for linear objectives over arbitrary sets for which we have a linear optimization oracle and as such generalizes previous work based on KKT-system decomposition and dualization approaches. The applicability of our framework for learning linear constraints is also discussed briefly. Our algorithm converges at a rate of O(1/sqrt(T)), and we demonstrate its effectiveness and applications in preliminary computational results.
APA:
Bärmann, A., Pokutta, S., & Schneider, O. (2017). Emulating the Expert: Inverse Optimization through Online Learning. In Precup D, Teh YW (Eds.), Proceedings of the 34th International Conference on Machine Learning (ICML) (pp. 400--410). International Convention Centre, Sydney, Australia: PMLR.
MLA:
Bärmann, Andreas, Sebastian Pokutta, and Oskar Schneider. "Emulating the Expert: Inverse Optimization through Online Learning." Proceedings of the Proceedings of the 34th International Conference on Machine Learning (ICML) Ed. Precup D, Teh YW, International Convention Centre, Sydney, Australia: PMLR, 2017. 400--410.
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