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We propose a novel adaptive approximation approach for test-time resource-constrained prediction.
Classification and regression trees
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Jerome H. Friedman · 2000
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Proceedings of the Yahoo! Learning to Rank Challenge, held at ICML 2010, Haifa, Israel, June 25, 2010
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The importance of encoding versus training with sparse coding and vector quantization
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Zhixiang Eddie Xu, Kilian Q. Weinberger, and Olivier Chapelle · 2012
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K Trapeznikov and V Saligrama · 2013
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Cost-sensitive tree of classifiers
Z Xu, M Kusner, M Chen, and K. Q Weinberger · 2013
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Feature-cost sensitive learning with submodular trees of classifiers
M Kusner, W Chen, Q Zhou, E Zhixiang, K Weinberger, and Y Chen · 2014
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Model Selection by Linear Programming
Joseph Wang, Tolga Bolukbasi, Kirill Trapeznikov, and Venkatesh Saligrama · 2014
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Feature-budgeted random forest
Feng Nan, Joseph Wang, and Venkatesh Saligrama · 2015
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Efficient learning by directed acyclic graph for resource constrained prediction
Joseph Wang, Kirill Trapeznikov, and Venkatesh Saligrama · 2015
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Unifying distillation and privileged information
D. Lopez-Paz, B. Schölkopf, L. Bottou, and V. Vapnik · 2016
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Dynamic structured model selection
D. Weiss, B. Sapp, and B. Taskar · 2013
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Pruning random forests for prediction on a budget
Feng Nan, Joseph Wang, and Venkatesh Saligrama · 2016
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