Robust Auto-Bidding Strategies for Online Advertising

In online advertising, existing auto-bidding strategies for bid shading mainly adopt the approach of first predicting the winning price distribution and then calculating the optimal bid. However, the winning price information available to the Demand Side Platforms (DSPs) is extremely limited, and the associated uncertainties make it challenging for DSPs to accurately estimate winning price distribution. To address this challenge, we conducted a comprehensive analysis of the process by which DSPs obtain winning price information, and abstracted two types of uncertainties from it: known uncertainty and unknown uncertainty. Based on these uncertainties, we proposed two levels of robust bidding strategies: Robust Bidding for Censorship (RBC) and Robust Bidding for Distribution Shift (RBDS), which offer guarantees for the surplus in the worst-case scenarios under uncertain conditions. Experimental results on public datasets demonstrate that our robust bidding strategies consistently enable DSPs to achieve superior surpluses, both on test sets and under worst-case conditions.