  We introduce a new numerical framework to learn optimal bidding strategies in
repeated auctions when the seller uses past bids to optimize her mechanism.
Crucially, we do not assume that the bidders know what optimization mechanism
is used by the seller. We recover essentially all state-of-the-art analytical
results for the single-item framework derived previously in the setup where the
bidder knows the optimization mechanism used by the seller and extend our
approach to multi-item settings, in which no optimal shading strategies were
previously known. Our approach yields substantial increases in bidder utility
in all settings. Our approach also has a strong potential for practical usage
since it provides a simple way to optimize bidding strategies on modern
marketplaces where buyers face unknown data-driven mechanisms.
