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Poster
McRank: Learning to Rank Using Multiple Classification and Gradient Boosting
Ping Li · Chris J Burges · Qiang Wu
Author Information
Ping Li (Baidu Research USA)
Chris J Burges (Microsoft Research)
Qiang Wu (Department of Computer Science and Engineering)
Related Events (a corresponding poster, oral, or spotlight)
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2007 Spotlight: McRank: Learning to Rank Using Multiple Classification and Gradient Boosting »
Tue. Dec 4th 11:20 -- 11:30 PM Room
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2017 Poster: Partial Hard Thresholding: Towards A Principled Analysis of Support Recovery »
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2016 Poster: Exact Recovery of Hard Thresholding Pursuit »
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2015 Poster: b-bit Marginal Regression »
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2015 Spotlight: b-bit Marginal Regression »
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2014 Workshop: Learning Semantics »
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2014 Poster: Asymmetric LSH (ALSH) for Sublinear Time Maximum Inner Product Search (MIPS) »
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2014 Poster: Recovery of Coherent Data via Low-Rank Dictionary Pursuit »
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2014 Poster: Online Optimization for Max-Norm Regularization »
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2013 Poster: Beyond Pairwise: Provably Fast Algorithms for Approximate $k$-Way Similarity Search »
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2013 Poster: Sign Cauchy Projections and Chi-Square Kernel »
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2013 Session: Oral Session 7 »
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2012 Poster: Entropy Estimations Using Correlated Symmetric Stable Random Projections »
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2012 Poster: One Permutation Hashing »
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2011 Poster: Hashing Algorithms for Large-Scale Learning »
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2010 Spotlight: b-Bit Minwise Hashing for Estimating Three-Way Similarities »
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2010 Poster: b-Bit Minwise Hashing for Estimating Three-Way Similarities »
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2009 Workshop: Advances in Ranking »
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2008 Poster: Localized Sliced Inverse Regression »
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2007 Poster: A Unified Near-Optimal Estimator For Dimension Reduction in $l_\alpha$ ($0<\alpha\leq 2$) Using Sta »
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2006 Poster: Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data »
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