Online Learning to Rank with Feedback at the Top
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[1] Thorsten Joachims,et al. Interactively optimizing information retrieval systems as a dueling bandits problem , 2009, ICML '09.
[2] Djoerd Hiemstra,et al. A cross-benchmark comparison of 87 learning to rank methods , 2015, Inf. Process. Manag..
[3] Aditya Bhaskara,et al. Approximating matrix p-norms , 2010, SODA '11.
[4] Tong Zhang,et al. Statistical Analysis of Bayes Optimal Subset Ranking , 2008, IEEE Transactions on Information Theory.
[5] Tie-Yan Liu,et al. Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.
[6] Christian Schindelhauer,et al. Discrete Prediction Games with Arbitrary Feedback and Loss , 2001, COLT/EuroCOLT.
[7] Sébastien Bubeck,et al. Multi-scale exploration of convex functions and bandit convex optimization , 2015, COLT.
[8] Frank Thomson Leighton,et al. The value of knowing a demand curve: bounds on regret for online posted-price auctions , 2003, 44th Annual IEEE Symposium on Foundations of Computer Science, 2003. Proceedings..
[9] Csaba Szepesvári,et al. Partial Monitoring - Classification, Regret Bounds, and Algorithms , 2014, Math. Oper. Res..
[10] Katja Hofmann,et al. Information Retrieval manuscript No. (will be inserted by the editor) Balancing Exploration and Exploitation in Listwise and Pairwise Online Learning to Rank for Information Retrieval , 2022 .
[11] Pradeep Ravikumar,et al. On NDCG Consistency of Listwise Ranking Methods , 2011, AISTATS.
[12] Ambuj Tewari,et al. Online Ranking with Top-1 Feedback , 2014, AISTATS.
[13] Thorsten Joachims,et al. Optimizing search engines using clickthrough data , 2002, KDD.
[14] Tong Zhang,et al. Subset Ranking Using Regression , 2006, COLT.
[15] Filip Radlinski,et al. Learning diverse rankings with multi-armed bandits , 2008, ICML '08.
[16] Martin Zinkevich,et al. Online Convex Programming and Generalized Infinitesimal Gradient Ascent , 2003, ICML.
[17] Filip Radlinski,et al. A support vector method for optimizing average precision , 2007, SIGIR.
[18] Claudio Gentile,et al. On multilabel classification and ranking with bandit feedback , 2014, J. Mach. Learn. Res..
[19] O. Chapelle. Large margin optimization of ranking measures , 2007 .
[20] Patrick Gallinari,et al. "On the (Non-)existence of Convex, Calibrated Surrogate Losses for Ranking" , 2012, NIPS.
[21] Adam Tauman Kalai,et al. Online convex optimization in the bandit setting , 2005, SODA 2005.
[22] Michael I. Jordan,et al. Convexity, Classification, and Risk Bounds , 2006 .
[23] Csaba Szepesvári,et al. Partial Monitoring with Side Information , 2012, ALT.
[24] Mingrui Wu,et al. Gradient descent optimization of smoothed information retrieval metrics , 2010, Information Retrieval.
[25] Tie-Yan Liu,et al. Learning to rank for information retrieval , 2009, SIGIR.
[26] Nicolò Cesa-Bianchi,et al. Regret Minimization Under Partial Monitoring , 2006, 2006 IEEE Information Theory Workshop - ITW '06 Punta del Este.
[27] Mark Sanderson,et al. Test Collection Based Evaluation of Information Retrieval Systems , 2010, Found. Trends Inf. Retr..
[28] Yi Chang,et al. Yahoo! Learning to Rank Challenge Overview , 2010, Yahoo! Learning to Rank Challenge.