Learning Theory and Algorithms for revenue optimization in second price auctions with reserve

Mehryar Mohri, Andres Munoz Medina
; Proceedings of the 31st International Conference on Machine Learning, PMLR 32(1):262-270, 2014.

Abstract

Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function (it is non-convex and discontinuous). We further give novel algorithms for solving this problem and report the results of encouraging experiments demonstrating their effectiveness.

Cite this Paper


BibTeX
@InProceedings{pmlr-v32-mohri14, title = {Learning Theory and Algorithms for revenue optimization in second price auctions with reserve}, author = {Mehryar Mohri and Andres Munoz Medina}, booktitle = {Proceedings of the 31st International Conference on Machine Learning}, pages = {262--270}, year = {2014}, editor = {Eric P. Xing and Tony Jebara}, volume = {32}, number = {1}, series = {Proceedings of Machine Learning Research}, address = {Bejing, China}, month = {22--24 Jun}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v32/mohri14.pdf}, url = {http://proceedings.mlr.press/v32/mohri14.html}, abstract = {Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function (it is non-convex and discontinuous). We further give novel algorithms for solving this problem and report the results of encouraging experiments demonstrating their effectiveness.} }
Endnote
%0 Conference Paper %T Learning Theory and Algorithms for revenue optimization in second price auctions with reserve %A Mehryar Mohri %A Andres Munoz Medina %B Proceedings of the 31st International Conference on Machine Learning %C Proceedings of Machine Learning Research %D 2014 %E Eric P. Xing %E Tony Jebara %F pmlr-v32-mohri14 %I PMLR %J Proceedings of Machine Learning Research %P 262--270 %U http://proceedings.mlr.press %V 32 %N 1 %W PMLR %X Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function (it is non-convex and discontinuous). We further give novel algorithms for solving this problem and report the results of encouraging experiments demonstrating their effectiveness.
RIS
TY - CPAPER TI - Learning Theory and Algorithms for revenue optimization in second price auctions with reserve AU - Mehryar Mohri AU - Andres Munoz Medina BT - Proceedings of the 31st International Conference on Machine Learning PY - 2014/01/27 DA - 2014/01/27 ED - Eric P. Xing ED - Tony Jebara ID - pmlr-v32-mohri14 PB - PMLR SP - 262 DP - PMLR EP - 270 L1 - http://proceedings.mlr.press/v32/mohri14.pdf UR - http://proceedings.mlr.press/v32/mohri14.html AB - Second-price auctions with reserve play a critical role for modern search engine and popular online sites since the revenue of these companies often directly depends on the outcome of such auctions. The choice of the reserve price is the main mechanism through which the auction revenue can be influenced in these electronic markets. We cast the problem of selecting the reserve price to optimize revenue as a learning problem and present a full theoretical analysis dealing with the complex properties of the corresponding loss function (it is non-convex and discontinuous). We further give novel algorithms for solving this problem and report the results of encouraging experiments demonstrating their effectiveness. ER -
APA
Mohri, M. & Medina, A.M.. (2014). Learning Theory and Algorithms for revenue optimization in second price auctions with reserve. Proceedings of the 31st International Conference on Machine Learning, in PMLR 32(1):262-270

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