  Online auctions play a central role in online advertising, and are one of the
main reasons for the industry's scalability and growth. With great changes in
how auctions are being organized, such as changing the second- to first-price
auction type, advertisers and demand platforms are compelled to adapt to a new
volatile environment. Bid shading is a known technique for preventing
overpaying in auction systems that can help maintain the strategy equilibrium
in first-price auctions, tackling one of its greatest drawbacks. In this study,
we propose a machine learning approach of modeling optimal bid shading for
non-censored online first-price ad auctions. We clearly motivate the approach
and extensively evaluate it in both offline and online settings on a major
demand side platform. The results demonstrate the superiority and robustness of
the new approach as compared to the existing approaches across a range of
performance metrics.
