Publication: Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment
Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment
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Weissteiner, J., Heiss, J., Siems, J., & Seuken, S. (2022). Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment. 541–548. https://doi.org/10.24963/ijcai.2022/77
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Many important resource allocation problems involve the combinatorial assignment of items, e.g., auctions or course allocation. Because the bundle space grows exponentially in the number of items, preference elicitation is a key challenge in these domains. Recently, researchers have proposed ML-based mechanisms that outperform traditional mechanisms while reducing preference elicitation costs for agents. However, one major shortcoming of the ML algorithms that were used is their disregard of important prior knowledge about agents' pre
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Weissteiner, J., Heiss, J., Siems, J., & Seuken, S. (2022). Monotone-Value Neural Networks: Exploiting Preference Monotonicity in Combinatorial Assignment. 541–548. https://doi.org/10.24963/ijcai.2022/77