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Xiangning Wang

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SODA Conference 2020 Conference Paper

Algorithmic Price Discrimination

Rachel Cummings
Nikhil R. Devanur
Zhiyi Huang 0002
Xiangning Wang

We consider a generalization of the third degree price discrimination problem studied in [4](Bergemann et al. , 2015), where an intermediary between the buyer and the seller can design market segments to maximize any linear combination of consumer surplus and seller revenue. Unlike in [4], we assume that the intermediary only has partial information about the buyer's value. We consider three different models of information, with increasing order of difficulty. In the first model, we assume that the intermediary's information allows him to construct a probability distribution of the buyer's value. Next we consider the sample complexity model, where we assume that the intermediary only sees samples from this distribution. Finally, we consider a bandit online learning model, where the intermediary can only observe past purchasing decisions of the buyer, rather than her exact value. For each of these models, we present algorithms to compute optimal or near optimal market segmentation.

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NeurIPS Conference 2018 Conference Paper

Learning Optimal Reserve Price against Non-myopic Bidders

Jinyan Liu
Zhiyi Huang
Xiangning Wang

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.

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