Hastie, Trevor J.

12 publications

JMLR 2022 Generalized Matrix Factorization: Efficient Algorithms for Fitting Generalized Linear Latent Variable Models to Large Data Arrays Lukasz Kidzinski, Francis K.C. Hui, David I. Warton, Trevor J. Hastie

ICLR 2016 Data Representation and Compression Using Linear-Programming Approximations Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie

UAI 2015 Fast Algorithms for Learning with Long N-Grams via Suffix Tree Based Matrix Multiplication Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie

AISTATS 2014 An Efficient Algorithm for Large Scale Compressive Feature Learning Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie

NeurIPS 2008 One Sketch for All: Theory and Application of Conditional Random Sampling Ping Li, Kenneth W. Church, Trevor J. Hastie

NeurIPS 2007 A Unified Near-Optimal Estimator for Dimension Reduction in $l_\alpha$ ($0<\alpha\leq 2$) Using Stable Random Projections Ping Li, Trevor J. Hastie

JMLR 2007 Nonlinear Estimators and Tail Bounds for Dimension Reduction in L1 Using Cauchy Random Projections Ping Li, Trevor J. Hastie, Kenneth W. Church

NeurIPS 2006 Conditional Random Sampling: A Sketch-Based Sampling Technique for Sparse Data Ping Li, Kenneth W. Church, Trevor J. Hastie

NeurIPS 2004 A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning Saharon Rosset, Ji Zhu, Hui Zou, Trevor J. Hastie

NeurIPS 2004 The Entire Regularization Path for the Support Vector Machine Saharon Rosset, Robert Tibshirani, Ji Zhu, Trevor J. Hastie

NeurIPS 2003 1-Norm Support Vector Machines Ji Zhu, Saharon Rosset, Robert Tibshirani, Trevor J. Hastie

NeurIPS 2003 Margin Maximizing Loss Functions Saharon Rosset, Ji Zhu, Trevor J. Hastie