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We study the problem of learning a linear model to set the reserve price in an auction, given contextual information, in order to maximize expected revenue from the seller side.
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Richard M Karp · 1975
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Michael Ostrovsky and Michael Schwarz · 2011
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Yijia Chen, Kord Eickmeyer, and Jörg Flum · 2012
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Daniel Mellinger, Alex Kushleyev, and Vijay Kumar · 2012
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Learning prices for repeated auctions with strategic buyers
Kareem Amin, Afshin Rostamizadeh, and Umar Syed · 2013
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Mark Braverman, Young Kun Ko, and Omri Weinstein · 2014
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Regret minimization for reserve prices in second-price auctions
Nicolo Cesa-Bianchi, Claudio Gentile, and Yishay Mansour · 2014
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A tight algorithm for strongly connected steiner subgraph on two terminals with demands
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Juan Pablo Vielma · 2015
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Scott Kuindersma, Robin Deits, Maurice Fallon, Andrés Valenzuela, Hongkai Dai, Frank Permenter, Twan Koolen, Pat Marion, and Russ Tedrake · 2016
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Eth hardness for densest-k-subgraph with perfect completeness
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Iain Dunning, Joey Huchette, and Miles Lubin · 2017
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Dynamic reserve prices for repeated auctions: Learning from bids
Yash Kanoria and Hamid Nazerzadeh · 2017
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Almost-polynomial ratio eth-hardness of approximating densest k-subgraph
Pasin Manurangsi · 2017
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(gap/s) eth hardness of svp
Divesh Aggarwal and Noah Stephens-Davidowitz · 2018
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Strong mixed-integer programming formulations for trained neural networks
Ross Anderson, Joey Huchette, Christian Tjandraatmadja, and Juan Pablo Vielma · 2019
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Moonkyung Ryu, Yinlam Chow, Ross Anderson, Christian Tjandraatmadja, and Craig Boutilier · 2019
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Gurobi optimizer reference manual, 2020
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