Optimal-er Auctions through Attention
- URL: http://arxiv.org/abs/2202.13110v1
- Date: Sat, 26 Feb 2022 10:47:12 GMT
- Title: Optimal-er Auctions through Attention
- Authors: Dmitry Ivanov, Iskander Safiulin, Ksenia Balabaeva, Igor Filippov
- Abstract summary: We propose two independent modifications of RegretNet, namely a new neural architecture based on the attention mechanism, denoted as TransRegret, and an alternative loss function that is interpretable.
In all experiments, we find that TransRegret consistently outperforms existing architectures in revenue.
- Score: 3.1423836318272773
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: 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 and quantify the Incentive Compatibility
constraint (that participants benefit from bidding truthfully). As a follow-up
to its success, we propose two independent modifications of RegretNet, namely a
new neural architecture based on the attention mechanism, denoted as
TransRegret, and an alternative loss function that is interpretable and
significantly less sensitive to hyperparameters. We investigate both proposed
modifications in an extensive experimental study in settings with fixed and
varied input sizes and additionally test out-of-setting generalization of our
network. In all experiments, we find that TransRegret consistently outperforms
existing architectures in revenue. Regarding our loss modification, we confirm
its effectiveness at controlling the revenue-regret trade-off by varying a
single interpretable hyperparameter.
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