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Learning Valuation Functions

In this paper we study the approximate learnability of valuations commonly used throughout economics and game theory for the quantitative encoding of agent preferences. We provide upper and lower bounds regarding the learnability of important subclasses of valuation functions that express no-complementarities. Our main results concern their approximate learnability in the distributional learning (PAC-style) setting. We provide nearly tight lower and upper bounds of $\tildeΘ(n^{1/2})$ on the approximation factor for learning XOS and subadditive valuations, both widely studied superclasses of submodular valuations. Interestingly, we show that the $\tildeΩ(n^{1/2})$ lower bound can be circumvented for XOS functions of polynomial complexity; we provide an algorithm for learning the class of XOS valuations with a representation of polynomial size achieving an $O(n^{\eps})$ approximation factor in time $O(n^{1/\eps})$ for any $\eps > 0$. This highlights the importance of considering the complexity of the target function for polynomial time learning. We also provide new learning results for interesting subclasses of submodular functions. Our upper bounds for distributional learning levera