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.

翻译：本文提出了一种新颖的机制设计方法，用于处理竞拍者类型分布不确定的多物品拍卖场景。所提出的方法利用非参数密度估计从历史出价中准确估计竞拍者类型，并基于Vickrey-Clarke-Groves（VCG）机制构建，确保了贝叶斯激励相容性（BIC）和δ-个体理性（IR）条件的满足。为进一步提升机制效率，我们引入了两种新颖的查询缩减策略：一种过滤方法，在估计分布生成的置信区间内筛选潜在获胜者的价值区间；另一种分类策略，当区间长度低于阈值时，将该区间的下界指定为估计类型。在小型和大型数据集上进行的仿真实验表明，我们的机制在收益最大化和查询缩减方面始终优于现有方法，尤其在大规模场景下表现突出。这使得所提出的机制成为多物品拍卖领域中卖家高度期望且有效的选择。