Linear feature extraction for ranking

[1]  Carlo Tomasi,et al.  Singular Value Decomposition , 2021, Encyclopedia of Social Network Analysis and Mining.

[2]  M. de Rijke,et al.  Deep Learning with Logged Bandit Feedback , 2018, ICLR.

[3]  M. de Rijke,et al.  Multileave Gradient Descent for Fast Online Learning to Rank , 2016, WSDM.

[4]  Ke Wang,et al.  A Cooperative Coevolution Framework for Parallel Learning to Rank , 2015, IEEE Transactions on Knowledge and Data Engineering.

[5]  Alessandro Moschitti,et al.  Learning to Rank Short Text Pairs with Convolutional Deep Neural Networks , 2015, SIGIR.

[6]  Chiranjib Bhattacharyya,et al.  Learning on graphs using Orthonormal Representation is Statistically Consistent , 2014, NIPS.

[7]  Josiane Mothe,et al.  Nonconvex Regularizations for Feature Selection in Ranking With Sparse SVM , 2014, IEEE Transactions on Neural Networks and Learning Systems.

[8]  Tatsuya Harada,et al.  Probabilistic Partial Canonical Correlation Analysis , 2014, ICML.

[9]  Gal Chechik,et al.  Coordinate-descent for learning orthogonal matrices through Givens rotations , 2014, ICML.

[10]  Ismail Sengör Altingövde,et al.  Exploiting Result Diversification Methods for Feature Selection in Learning to Rank , 2014, ECIR.

[11]  Balázs Kégl,et al.  Tune and mix: learning to rank using ensembles of calibrated multi-class classifiers , 2013, Machine Learning.

[12]  Tao Qin,et al.  Introducing LETOR 4.0 Datasets , 2013, ArXiv.

[13]  Yong Tang,et al.  FSMRank: Feature Selection Algorithm for Learning to Rank , 2013, IEEE Transactions on Neural Networks and Learning Systems.

[14]  Tie-Yan Liu,et al.  Statistical Consistency of Ranking Methods in A Rank-Differentiable Probability Space , 2012, NIPS.

[15]  Paolo Rosso,et al.  Expected Divergence Based Feature Selection for Learning to Rank , 2012, COLING.

[16]  Xueqi Cheng,et al.  Top-k learning to rank: labeling, ranking and evaluation , 2012, SIGIR '12.

[17]  Tao Qin,et al.  LETOR: A benchmark collection for research on learning to rank for information retrieval , 2010, Information Retrieval.

[18]  Tie-Yan Liu,et al.  Future directions in learning to rank , 2010, Yahoo! Learning to Rank Challenge.

[19]  Rong Jin,et al.  Learning to Rank by Optimizing NDCG Measure , 2009, NIPS.

[20]  Feng Pan,et al.  Feature selection for ranking using boosted trees , 2009, CIKM.

[21]  Wook-Shin Han,et al.  Efficient feature weighting methods for ranking , 2009, CIKM.

[22]  Tie-Yan Liu,et al.  Ranking Measures and Loss Functions in Learning to Rank , 2009, NIPS.

[23]  Thorsten Joachims,et al.  Cutting-plane training of structural SVMs , 2009, Machine Learning.

[24]  Yehuda Koren,et al.  Matrix Factorization Techniques for Recommender Systems , 2009, Computer.

[25]  Maksims Volkovs,et al.  BoltzRank: learning to maximize expected ranking gain , 2009, ICML '09.

[26]  Tie-Yan Liu,et al.  Learning to rank for information retrieval , 2009, SIGIR.

[27]  Tong Zhang,et al.  Statistical Analysis of Bayes Optimal Subset Ranking , 2008, IEEE Transactions on Information Theory.

[28]  Qiang Wu,et al.  McRank: Learning to Rank Using Multiple Classification and Gradient Boosting , 2007, NIPS.

[29]  Tie-Yan Liu,et al.  Learning to rank for information retrieval (LR4IR 2007) , 2007, SIGF.

[30]  Donald Metzler,et al.  Automatic feature selection in the markov random field model for information retrieval , 2007, CIKM '07.

[31]  Tao Qin,et al.  FRank: a ranking method with fidelity loss , 2007, SIGIR.

[32]  Hang Li,et al.  AdaRank: a boosting algorithm for information retrieval , 2007, SIGIR.

[33]  Filip Radlinski,et al.  A support vector method for optimizing average precision , 2007, SIGIR.

[34]  Tao Qin,et al.  Feature selection for ranking , 2007, SIGIR.

[35]  Tie-Yan Liu,et al.  Learning to rank: from pairwise approach to listwise approach , 2007, ICML '07.

[36]  Quoc V. Le,et al.  Learning to Rank with Nonsmooth Cost Functions , 2006, NIPS.

[37]  Tie-Yan Liu,et al.  Adapting ranking SVM to document retrieval , 2006, SIGIR.

[38]  Gregory N. Hullender,et al.  Learning to rank using gradient descent , 2005, ICML.

[39]  Lior Wolf,et al.  Combining variable selection with dimensionality reduction , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[40]  John Shawe-Taylor,et al.  Canonical Correlation Analysis: An Overview with Application to Learning Methods , 2004, Neural Computation.

[41]  A. Ng.  Feature selection, L1 vs. L2 regularization, and rotational invariance , 2004, Twenty-first international conference on Machine learning - ICML '04.

[42]  Jaana Kekäläinen,et al.  Cumulated gain-based evaluation of IR techniques , 2002, TOIS.

[43]  Koby Crammer,et al.  Pranking with Ranking , 2001, NIPS.

[44]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[45]  J. Tenenbaum,et al.  A global geometric framework for nonlinear dimensionality reduction. , 2000, Science.

[46]  Yoram Singer,et al.  An Efficient Boosting Algorithm for Combining Preferences by , 2013 .

[47]  Bernhard Schölkopf,et al.  Nonlinear Component Analysis as a Kernel Eigenvalue Problem , 1998, Neural Computation.

[48]  Pat Langley,et al.  Selection of Relevant Features and Examples in Machine Learning , 1997, Artif. Intell..

[49]  G. Arfken.  Mathematical Methods for Physicists , 1967 .

[50]  Maurice G. Kendall,et al.  Rank Correlation Methods , 1949 .

[51]  Heng Tao Shen,et al.  Principal Component Analysis , 2009, Encyclopedia of Biometrics.

[52]  Michael I. Jordan,et al.  A Probabilistic Interpretation of Canonical Correlation Analysis , 2005 .

[53]  Hiroshi Motoda,et al.  Feature Selection Extraction and Construction , 2002 .

[54]  Sayan Mukherjee,et al.  Feature Selection for SVMs , 2000, NIPS.

[55]  Michael E. Tipping,et al.  Probabilistic Principal Component Analysis , 1999 .

[56]  D. Bertsekas.  Nonlinear Programming , 1995 .

[57]  J. Platt,et al.  Constrained Differential Optimization for Neural Networks , 1988 .

[58]  A. K. Jain,et al.  A critical evaluation of intrinsic dimensionality algorithms. , 1980 .