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Martial Hebert | J. Andrew Bagnell | Alexander Grubb | Hanzhang Hu | M. Hebert | J. Bagnell | Alexander Grubb | Hanzhang Hu
[1] A. Atkinson. Subset Selection in Regression , 1992 .
[2] Y. C. Pati,et al. Orthogonal matching pursuit: recursive function approximation with applications to wavelet decomposition , 1993, Proceedings of 27th Asilomar Conference on Signals, Systems and Computers.
[3] Stéphane Mallat,et al. Matching pursuits with time-frequency dictionaries , 1993, IEEE Trans. Signal Process..
[4] Shlomo Zilberstein,et al. Anytime algorithm development tools , 1996, SGAR.
[5] R. Tibshirani. Regression Shrinkage and Selection via the Lasso , 1996 .
[6] Yoshua Bengio,et al. Gradient-based learning applied to document recognition , 1998, Proc. IEEE.
[7] Paul A. Viola,et al. Rapid object detection using a boosted cascade of simple features , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.
[8] Robert E. Schapire,et al. The Boosting Approach to Machine Learning An Overview , 2003 .
[9] Jiri Matas,et al. WaldBoost - learning for time constrained sequential detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).
[10] M. Yuan,et al. Model selection and estimation in regression with grouped variables , 2006 .
[11] Susan A. Murphy,et al. Monographs on statistics and applied probability , 1990 .
[12] James M. Rehg,et al. On the Design of Cascades of Boosted Ensembles for Face Detection , 2008, International Journal of Computer Vision.
[13] Matthew J. Streeter,et al. An Online Algorithm for Maximizing Submodular Functions , 2008, NIPS.
[14] Kilian Q. Weinberger,et al. Feature hashing for large scale multitask learning , 2009, ICML '09.
[15] Tong Zhang,et al. On the Consistency of Feature Selection using Greedy Least Squares Regression , 2009, J. Mach. Learn. Res..
[16] Naoki Abe,et al. Grouped Orthogonal Matching Pursuit for Variable Selection and Prediction , 2009, NIPS.
[17] Julien Mairal,et al. Proximal Methods for Sparse Hierarchical Dictionary Learning , 2010, ICML.
[18] François Fleuret,et al. Joint Cascade Optimization Using A Product Of Boosted Classifiers , 2010, NIPS.
[19] Naoki Abe,et al. Group Orthogonal Matching Pursuit for Logistic Regression , 2011, AISTATS.
[20] Yi Chang,et al. Yahoo! Learning to Rank Challenge Overview , 2010, Yahoo! Learning to Rank Challenge.
[21] Proceedings of the Yahoo! Learning to Rank Challenge, held at ICML 2010, Haifa, Israel, June 25, 2010 , 2011, Yahoo! Learning to Rank Challenge.
[22] Lev Reyzin,et al. Boosting on a Budget: Sampling for Feature-Efficient Prediction , 2011, ICML.
[23] Abhimanyu Das,et al. Submodular meets Spectral: Greedy Algorithms for Subset Selection, Sparse Approximation and Dictionary Selection , 2011, ICML.
[24] Trevor Darrell,et al. Timely Object Recognition , 2012, NIPS.
[25] Kilian Q. Weinberger,et al. Classifier Cascade for Minimizing Feature Evaluation Cost , 2012, AISTATS.
[26] Kilian Q. Weinberger,et al. The Greedy Miser: Learning under Test-time Budgets , 2012, ICML.
[27] J. Andrew Bagnell,et al. SpeedBoost: Anytime Prediction with Uniform Near-Optimality , 2012, AISTATS.
[28] Matt J. Kusner,et al. Anytime Representation Learning , 2013, ICML.
[29] Matt J. Kusner,et al. Classifier cascades and trees for minimizing feature evaluation cost , 2014, J. Mach. Learn. Res..
[30] Andreas Krause,et al. Submodular Function Maximization , 2014, Tractability.
[31] Nuno Vasconcelos,et al. Learning Complexity-Aware Cascades for Deep Pedestrian Detection , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).