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

Bid Shading in The Brave New World of First-Price Auctions