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Multi-Item Mechanisms without Item-Independence: Learnability via Robustness
[article]
2020
arXiv
pre-print
We study the sample complexity of learning revenue-optimal multi-item auctions. We obtain the first set of positive results that go beyond the standard but unrealistic setting of item-independence. In particular, we consider settings where bidders' valuations are drawn from correlated distributions that can be captured by Markov Random Fields or Bayesian Networks – two of the most prominent graphical models. We establish parametrized sample complexity bounds for learning an up-to-ε optimal
arXiv:1911.02146v2
fatcat:6y2ojwm27vdc5i4f4jwiwl7a44