  This paper presents a novel mechanism design for multi-item auction settings
with uncertain bidders' type distributions. Our proposed approach utilizes
nonparametric density estimation to accurately estimate bidders' types from
historical bids, and is built upon the Vickrey-Clarke-Groves (VCG) mechanism,
ensuring satisfaction of Bayesian incentive compatibility (BIC) and
$\delta$-individual rationality (IR). To further enhance the efficiency of our
mechanism, we introduce two novel strategies for query reduction: a filtering
method that screens potential winners' value regions within the confidence
intervals generated by our estimated distribution, and a classification
strategy that designates the lower bound of an interval as the estimated type
when the length is below a threshold value. Simulation experiments conducted on
both small-scale and large-scale data demonstrate that our mechanism
consistently outperforms existing methods in terms of revenue maximization and
query reduction, particularly in large-scale scenarios. This makes our proposed
mechanism a highly desirable and effective option for sellers in the realm of
multi-item auctions.
