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Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising References Meier,Lukas,Van De Geer.Sara,and Buihlmann,Peter Agarwal,Deepak,Agrawal,Rahul,Khanna,Rajiv,and Ko- The group lasso for logistic regression.Journal of the ta,Nagaraj.Estimating rates of rare events with multiple Royal Statistical Society,Series B.70(1):53-71,2008. hierarchies through scalable log-linear models.In KDD, Menon,Aditya Krishna,Chitrapura,Krishna Prasad,Garg, pp.213-222,2010. Sachin,Agarwal,Deepak,and Kota,Nagaraj.Response Andrew,Galen and Gao,Jianfeng.Scalable training of prediction using collaborative filtering with hierarchies Ij-regularized log-linear models.In ICML,pp.33-40, and side-information.In KDD,pp.141-149,2011. 2007. Muthukrishnan,S.Ad exchanges:Research issues.In Bradley,Andrew P.The use of the area under the roc curve WNE,pp.1-12,2009. in the evaluation of machine learning algorithms.Pattern Recognition,30(7):1145-1159,1997. Neter,John,Wasserman,William,and Kutner,Michael H. Applied linear statistical models,volume 4.Irwin Chica- Broyden,C.G.The convergence of a class of double-rank g0,1996. minimization algorithms 1.general considerations.IMA Journal of Applied Mathematics,6(1):76-90,1970. Nocedal,Jorge.Updating quasi-newton matrices with lim- ited storage.Mathematics of Computation,35(151): Byrd,Richard H.,Nocedal,Jorge,and Schnabel,Robert B. 773-782.1980 Representations of quasi-newton matrices and their use in limited memory methods.Mathematical Program- Richardson,Matthew,Dominowska,Ewa,and Ragno, ming,63:129-156,1994. Robert.Predicting clicks:estimating the click-through rate for new ads.In WWW,pp.521-530,2007. Chapelle.Oliver,Manavoglu,Eren.and Rosales,Romer. Simple and scalable response prediction for display ad- Stern,David H.,Herbrich,Ralf,and Graepel,Thore. vertising.ACM Transactions on Intelligent Systems and Matchbox:large scale online bayesian recommendation- Technology,2013. s.nWWw,Pp.111-120,2009. Golub,Gene H,Hansen,Per Christian,and O'Leary,Di- Tibshirani,Robert.Regression shrinkage and selection via anne P.Tikhonov regularization and total least squares. the lasso.Journal of the Royal Statistical Society.Series SIAM Journal on Matrix Analysis and Applications,21 B.Pp.267-288,1996. (1):185-194.1999. Weinberger,Kilian Q.,Dasgupta,Anirban,Langford,John, Graepel,Thore,Candela,Joaquin Quinonero,Borchert. Smola,Alexander J.,and Attenberg,Josh.Feature hash- Thomas,and Herbrich,Ralf.Web-scale bayesian click- ing for large scale multitask learning.In ICML,pp.140, through rate prediction for sponsored search advertising 2009 in microsoft's bing search engine.In ICML,pp.13-20, 2010. Yuan,Ming and Lin,Yi.Model selection and estimation in regression with grouped variables.Journal of the Royal Kuang-chih,Lee,Orten,Burkay,Dasdan,Ali,and Li,Wen- Statistical Society.Series B.68:49-67,2006. tong.Estimating conversion rate in display advertis- ing from past performance data.In KDD,pp.768-776. 2012. Mahdian,Mohammad and Tomak,Kerem.Pay-per-action model for online advertising.In WINE,pp.549-557, 2007 Malouf,Robert.A comparison of algorithms for maximum entropy parameter estimation.In CoNLL,pp.49-55, 2002. McMahan,H.Brendan,Holt,Gary,Sculley,David,Y- oung,Michael,Ebner,Dietmar,Grady,Julian,Nie, Lan,Phillips,Todd,Davydov,Eugene,Golovin,Daniel, Chikkerur,Sharat,Liu,Dan,Wattenberg,Martin, Hrafnkelsson,Arnar Mar,Boulos,Tom,and Kubica, Jeremy.Ad click prediction:a view from the trenches. nKDD,pp.1222-1230,2013.Coupled Group Lasso for Web-Scale CTR Prediction in Display Advertising References Agarwal, Deepak, Agrawal, Rahul, Khanna, Rajiv, and Ko￾ta, Nagaraj. Estimating rates of rare events with multiple hierarchies through scalable log-linear models. In KDD, pp. 213–222, 2010. Andrew, Galen and Gao, Jianfeng. Scalable training of l1 -regularized log-linear models. In ICML, pp. 33–40, 2007. Bradley, Andrew P. The use of the area under the roc curve in the evaluation of machine learning algorithms. Pattern Recognition, 30(7):1145–1159, 1997. Broyden, C. G. The convergence of a class of double-rank minimization algorithms 1. general considerations. IMA Journal of Applied Mathematics, 6(1):76–90, 1970. Byrd, Richard H., Nocedal, Jorge, and Schnabel, Robert B. Representations of quasi-newton matrices and their use in limited memory methods. Mathematical Program￾ming, 63:129–156, 1994. Chapelle, Oliver, Manavoglu, Eren, and Rosales, Romer. Simple and scalable response prediction for display ad￾vertising. ACM Transactions on Intelligent Systems and Technology, 2013. Golub, Gene H, Hansen, Per Christian, and O’Leary, Di￾anne P. Tikhonov regularization and total least squares. SIAM Journal on Matrix Analysis and Applications, 21 (1):185–194, 1999. Graepel, Thore, Candela, Joaquin Quinonero, Borchert, ˜ Thomas, and Herbrich, Ralf. Web-scale bayesian click￾through rate prediction for sponsored search advertising in microsoft’s bing search engine. In ICML, pp. 13–20, 2010. Kuang-chih, Lee, Orten, Burkay, Dasdan, Ali, and Li, Wen￾tong. Estimating conversion rate in display advertis￾ing from past performance data. In KDD, pp. 768–776, 2012. Mahdian, Mohammad and Tomak, Kerem. Pay-per-action model for online advertising. In WINE, pp. 549–557, 2007. Malouf, Robert. A comparison of algorithms for maximum entropy parameter estimation. In CoNLL, pp. 49–55, 2002. McMahan, H. Brendan, Holt, Gary, Sculley, David, Y￾oung, Michael, Ebner, Dietmar, Grady, Julian, Nie, Lan, Phillips, Todd, Davydov, Eugene, Golovin, Daniel, Chikkerur, Sharat, Liu, Dan, Wattenberg, Martin, Hrafnkelsson, Arnar Mar, Boulos, Tom, and Kubica, Jeremy. Ad click prediction: a view from the trenches. In KDD, pp. 1222–1230, 2013. Meier, Lukas, Van De Geer, Sara, and Buhlmann, Peter. ¨ The group lasso for logistic regression. Journal of the Royal Statistical Society, Series B, 70(1):53–71, 2008. Menon, Aditya Krishna, Chitrapura, Krishna Prasad, Garg, Sachin, Agarwal, Deepak, and Kota, Nagaraj. Response prediction using collaborative filtering with hierarchies and side-information. In KDD, pp. 141–149, 2011. Muthukrishnan, S. Ad exchanges: Research issues. In WINE, pp. 1–12, 2009. Neter, John, Wasserman, William, and Kutner, Michael H. Applied linear statistical models, volume 4. Irwin Chica￾go, 1996. Nocedal, Jorge. Updating quasi-newton matrices with lim￾ited storage. Mathematics of Computation, 35(151): 773–782, 1980. Richardson, Matthew, Dominowska, Ewa, and Ragno, Robert. Predicting clicks: estimating the click-through rate for new ads. In WWW, pp. 521–530, 2007. Stern, David H., Herbrich, Ralf, and Graepel, Thore. Matchbox: large scale online bayesian recommendation￾s. In WWW, pp. 111–120, 2009. Tibshirani, Robert. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society. Series B, pp. 267–288, 1996. Weinberger, Kilian Q., Dasgupta, Anirban, Langford, John, Smola, Alexander J., and Attenberg, Josh. Feature hash￾ing for large scale multitask learning. In ICML, pp. 140, 2009. Yuan, Ming and Lin, Yi. Model selection and estimation in regression with grouped variables. Journal of the Royal Statistical Society, Series B, 68:49–67, 2006
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