MEOW: A Space-Efficient Nonparametric Bid Shading Algorithm-Reference-Cited by-同舟云学术

MEOW: A Space-Efficient Nonparametric Bid Shading Algorithm

Published:2021-08-14 Issue: Volume: Page:
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Container-title:Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining
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Author:

Zhang Wei¹,Kitts Brendan²,Han Yanjun³,Zhou Zhengyuan⁴,Mao Tingyu⁵,He Hao⁵,Pan Shengjun⁵,Flores Aaron⁶,Gultekin San⁵,Weissman Tsachy³

Affiliation:

1. Tsinghua University, Beijing, China

2. Verizon, Seattle, WA, USA

3. Stanford University, Stanford, CA, USA

4. New York University, New York, NY, USA

5. Verizon, Sunnyvale, CA, USA

6. Yahoo! Research, Sunnyvale, CA, USA

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3447548.3467113

Reference45 articles.

1. 2021. Nonparametric statistics.Wikipedia website(2021). https://en.wikipedia.org/wiki/Nonparametric_statistics 2021. Nonparametric statistics.Wikipedia website(2021). https://en.wikipedia.org/wiki/Nonparametric_statistics

2. Estimating first-price auctions with an unknown number of bidders: A misclassification approach

3. AppNexus. 2018. Demystifying Auction Dynamics for Digital Buyers and Sellers. In AppNexus Website. https://www.appnexus.com/sites/default/files/whitepapers/49344-CM-Auction-Type-Whitepaper-V9.pdf AppNexus. 2018. Demystifying Auction Dynamics for Digital Buyers and Sellers. In AppNexus Website. https://www.appnexus.com/sites/default/files/whitepapers/49344-CM-Auction-Type-Whitepaper-V9.pdf

4. Rationalizable bidding in first-price auctions

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