Auctions are key for maximizing sellers' revenue and ensuring truthful
bidding among buyers. Recently, an approach known as differentiable economics
based on deep learning shows promise in learning optimal auction mechanisms for
multiple items and participants. However, this approach has no guarantee of
strategy-proofness at test time. Strategy-proofness is crucial as it ensures
that buyers are incentivized to bid their true valuations, leading to optimal
and fair auction outcomes without the risk of manipulation. Building on
conformal prediction, we introduce a novel approach to achieve
strategy-proofness with rigorous statistical guarantees. The key novelties of
our method are: (i) the formulation of a regret prediction model, used to
quantify at test time violations of strategy-proofness; and (ii) an auction
acceptance rule that leverages the predicted regret to ensure that for a new
auction, the data-driven mechanism meets the strategy-proofness requirement
with high probability (e.g., 99\%). Numerical experiments demonstrate the
necessity for rigorous guarantees, the validity of our theoretical results, and
the applicability of our proposed method.

利用可区分经济学、遗憾预测模型和一种能确保拍卖机制具有战略不可篡改性的拍卖接受规则，本研究提出了一种新方法来确保拍卖过程中买家被鼓励出真实估值，以实现最优且公平的拍卖结果，并通过数值实验证明了该方法的适用性和有效性。