Learning Optimal Reserve Price against Non-myopic Bidders
Learning Optimal Reserve Price against Non-myopic Bidders
复制标题
学习针对非短视投标人的最优保留价
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Xiangning Wang
中科院分区：
文献类型：
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作者：
Zhiyi Huang;Jinyan Liu;Xiangning Wang
我们考虑的问题，学习最优保留价在重复拍卖对非短视的投标人，谁可能会出价策略，以获得在未来几轮，即使单轮拍卖是真实的。先前的算法，例如，经验定价，不提供非平凡的遗憾轮在此设置一般。我们介绍的算法，获得对非短视投标人小遗憾时，市场很大，即，在各轮的恒定部分中，或者当投标人不耐烦时，即，他们会用某种稍微偏离一个的因子来贴现未来效用。我们的方法仔细控制了向每个投标人透露的信息，并建立在差分私有在线学习技术以及最近的联合差分私有算法的基础上。
We consider the problem of learning optimal reserve price in repeated auctions against non-myopic bidders, who may bid strategically in order to gain in future rounds even if the single-round auctions are truthful. Previous algorithms, e.g., empirical pricing, do not provide non-trivial regret rounds in this setting in general. We introduce algorithms that obtain small regret against non-myopic bidders either when the market is large, i.e., no bidder appears in a constant fraction of the rounds, or when the bidders are impatient, i.e., they discount future utility by some factor mildly bounded away from one. Our approach carefully controls what information is revealed to each bidder, and builds on techniques from differentially private online learning as well as the recent line of works on jointly differentially private algorithms.