Optimal-er Auctions through Attention. (arXiv:2202.13110v3 [cs.LG] UPDATED)

Web: http://arxiv.org/abs/2202.13110

RegretNet is a recent breakthrough in the automated design of
revenue-maximizing auctions. It combines the expressivity of deep learning with
the regret-based approach to relax the Incentive Compatibility constraint (that
participants benefit from bidding truthfully). We propose two independent
modifications of RegretNet, namely a neural architecture based on the attention
mechanism, denoted as RegretFormer, and an interpretable loss function that is
significantly less sensitive to hyperparameters. We investigate both proposed
modifications in an extensive experimental study that includes settings with …

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