Trevor Hastie

Publication Activity (10 Years)

Years Active: 1990-2024
Publications (10 Years): 22

Top Topics

Matrix Completion

Top Venues

J. Mach. Learn. Res.

J. Am. Medical Informatics Assoc.

Publications

Thomas Le Menestrel, Erin Craig, Robert Tibshirani, Trevor Hastie, Manuel A. Rivas
Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank. CoRR (2024)
Lukasz Kidzinski, Trevor Hastie
Modeling Longitudinal Data Using Matrix Completion. J. Comput. Graph. Stat. 33 (2) (2024)
James Yang, Trevor Hastie
A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. CoRR (2024)
Erin Craig, Timothy Keyes, Jolanda Sarno, Maxim Zaslavsky, Garry Nolan, Kara Davis, Trevor Hastie, Robert Tibshirani
MMIL: A novel algorithm for disease associated cell type discovery. CoRR (2024)
J. Kenneth Tay, Balasubramanian Narasimhan, Trevor Hastie
Elastic Net Regularization Paths for All Generalized Linear Models. J. Stat. Softw. 106 (1) (2023)
Tetiana Parshakova, Trevor Hastie, Eric Darve, Stephen Boyd
Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices. CoRR (2023)
Anav Sood, Trevor Hastie
A Statistical View of Column Subset Selection. CoRR (2023)
Ismael Lemhadri, Harrison H. Li, Trevor Hastie
RbX: Region-based explanations of prediction models. CoRR (2022)
Zijun Gao, Trevor Hastie
LinCDE: Conditional Density Estimation via Lindsey's Method. J. Mach. Learn. Res. 23 (2022)
Ruilin Li, Yosuke Tanigawa, Johanne M. Justesen, Jonathan Taylor, Trevor Hastie, Robert Tibshirani, Manuel A. Rivas
Survival analysis on rare events using group-regularized multi-response Cox regression. Bioinform. 37 (23) (2021)
Ruilin Li, Christopher Chang, Yosuke Tanigawa, Balasubramanian Narasimhan, Trevor Hastie, Robert Tibshirani, Manuel A. Rivas
Fast numerical optimization for genome sequencing data in population biobanks. Bioinform. 37 (22) (2021)
Elena Tuzhilina, Trevor Hastie
Weighted Low Rank Matrix Approximation and Acceleration. CoRR (2021)
Trevor Hastie
Ridge Regularizaton: an Essential Concept in Data Science. CoRR (2020)
Trevor Hastie
Ridge Regularization: An Essential Concept in Data Science. Technometrics 62 (4) (2020)
Anthony Culos, Amy Tsai, Natalie Stanley, Martin Becker, Mohammad Sajjad Ghaemi, David Mcilwain, Ramin Fallahzadeh, Athena Tanada, Huda Nassar, Camilo Espinosa, Maria Xenochristou, Edward Ganio, Laura Peterson, Xiaoyuan Han, Ina A. Stelzer, Kazuo Ando, Dyani Gaudilliere, Thanaphong Phongpreecha, Ivana Maric, Alan L. Chang, Gary M. Shaw, David K. Stevenson, Sean Bendall, Kara L. Davis, Wendy J. Fantl, Garry P. Nolan, Trevor Hastie, Robert Tibshirani, Martin S. Angst, Brice Gaudilliere, Nima Aghaeepour
Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. Nat. Mach. Intell. 2 (10) (2020)
J. Kenneth Tay, Nima Aghaeepour, Trevor Hastie, Robert Tibshirani
Feature-weighted elastic net: using "features of features" for better prediction. CoRR (2020)
Lukasz Kidzinski, Francis K. C. Hui, David I. Warton, Trevor Hastie
Generalized Matrix Factorization. CoRR (2020)
Yifang Liu, Zhentao Xu, Qiyuan An, Yang Yi, Yanzhi Wang, Trevor Hastie
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach. CoRR (2020)
Trevor Hastie, Andrea Montanari, Saharon Rosset, Ryan J. Tibshirani
Surprises in High-Dimensional Ridgeless Least Squares Interpolation. CoRR (2019)
Lukasz Kidzinski, Trevor Hastie
Longitudinal data analysis using matrix completion. CoRR (2018)
Yen S. Low, Aaron C. Daugherty, Elizabeth A. Schroeder, William Chen, Tina Seto, Susan C. Weber, Michael Lim, Trevor Hastie, Maya Mathur, Manisha Desai, Carl Farrington, Andrew A. Radin, Marina Sirota, Pragati Kenkare, Caroline A. Thompson, Peter P. Yu, Scarlett L. Gomez, George W. Sledge, Allison W. Kurian, Nigam H. Shah
Synergistic drug combinations from electronic health records and gene expression. J. Am. Medical Informatics Assoc. 24 (3) (2017)
Nicholas Boyd, Trevor Hastie, Stephen P. Boyd, Benjamin Recht, Michael I. Jordan
Saturating Splines and Feature Selection. J. Mach. Learn. Res. 18 (2017)
Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh
Matrix completion and low-rank SVD via fast alternating least squares. J. Mach. Learn. Res. 16 (2015)
Rakesh Achanta, Trevor Hastie
Telugu OCR Framework using Deep Learning. CoRR (2015)
Joy P. Ku, Jennifer L. Hicks, Trevor Hastie, Jure Leskovec, Christopher Ré, Scott L. Delp
The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility. J. Am. Medical Informatics Assoc. 22 (6) (2015)
Stefan Wager, Trevor Hastie, Bradley Efron
Confidence intervals for random forests: the jackknife and the infinitesimal jackknife. J. Mach. Learn. Res. 15 (1) (2014)
Jason D. Lee, Trevor Hastie
Structure Learning of Mixed Graphical Models. AISTATS (2013)
Rahul Mazumder, Trevor Hastie
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso. J. Mach. Learn. Res. 13 (2012)
Jason D. Lee, Trevor Hastie
Learning Mixed Graphical Models CoRR (2012)
Line H. Clemmensen, Trevor Hastie, Daniela M. Witten, Bjarne K. Ersbøll
Sparse Discriminant Analysis. Technometrics 53 (4) (2011)
Rahul Mazumder, Trevor Hastie
The Graphical Lasso: New Insights and Alternatives CoRR (2011)
Rahul Mazumder, Trevor Hastie, Robert Tibshirani
Spectral Regularization Algorithms for Learning Large Incomplete Matrices. J. Mach. Learn. Res. 11 (2010)
Silpa Suthram, Joel Dudley, Annie P. Chiang, Rong Chen, Trevor Hastie, Atul J. Butte
Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets. PLoS Comput. Biol. 6 (2) (2010)
Michael Greenacre, Trevor Hastie
Dynamic visualization of statistical learning in the context of high-dimensional textual data. J. Web Semant. 8 (2-3) (2010)
Tong Tong Wu, Yi Fang Chen, Trevor Hastie, Eric M. Sobel, Kenneth Lange
Genome-wide association analysis by lasso penalized logistic regression. Bioinform. 25 (6) (2009)
Ping Li, Kenneth Ward Church, Trevor Hastie
One sketch for all: Theory and Application of Conditional Random Sampling. NIPS (2008)
Trevor Hastie, Jerome H. Friedman, Robert Tibshirani
Regularization paths and coordinate descent. KDD (2008)
Ping Li, Trevor Hastie, Kenneth Ward Church
Using Cauchy Random Projections. COLT (2007)
Robert Tibshirani, Trevor Hastie
Margin Trees for High-dimensional Classification. J. Mach. Learn. Res. 8 (2007)
Ping Li, Trevor Hastie
(0 < alpha <= 2) Using Stable Random Projections. NIPS (2007)
Ping Li, Trevor Hastie, Kenneth Ward Church
Using Cauchy Random Projections. J. Mach. Learn. Res. 8 (2007)
Ping Li, Trevor Hastie, Kenneth Ward Church
Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$ Using Cauchy Random Projections CoRR (2006)
Ping Li, Kenneth Ward Church, Trevor Hastie
Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data. NIPS (2006)
Ping Li, Trevor Hastie, Kenneth Ward Church
Improving Random Projections Using Marginal Information. COLT (2006)
Ping Li, Trevor Hastie, Kenneth Ward Church
Very sparse random projections. KDD (2006)
Dirk Ormoneit, Michael J. Black, Trevor Hastie, Hedvig Kjellström
Representing cyclic human motion using functional analysis. Image Vis. Comput. 23 (14) (2005)
Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu
The Entire Regularization Path for the Support Vector Machine. J. Mach. Learn. Res. 5 (2004)
Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu
The Entire Regularization Path for the Support Vector Machine. NIPS (2004)
Philip Beineke, Trevor Hastie, Shivakumar Vaithyanathan
The Sentimental Factor: Improving Review Classification Via Human-Provided Information. ACL (2004)
Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, Scott G. Soltys, Gongyi Shi, Albert C. Koong, Quynh-Thu Le
Sample classification from protein mass spectrometry, by 'peak probability contrasts'. Bioinform. 20 (17) (2004)
Saharon Rosset, Ji Zhu, Trevor Hastie
Boosting as a Regularized Path to a Maximum Margin Classifier. J. Mach. Learn. Res. 5 (2004)
Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning. NIPS (2004)
Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani
1-norm Support Vector Machines. NIPS (2003)
Saharon Rosset, Ji Zhu, Trevor Hastie
Boosting and support vector machines as optimal separators. DRR (2003)
Trevor Hastie, Robert Tibshirani, Jerome H. Friedman
Note on "Comparison of Model Selection for Regression" by Vladimir Cherkassky and Yunqian Ma. Neural Comput. 15 (7) (2003)
Saharon Rosset, Ji Zhu, Trevor Hastie
Margin Maximizing Loss Functions. NIPS (2003)
Ji Zhu, Trevor Hastie
Support Vector Machines, Kernel Logistic Regression and Boosting. Multiple Classifier Systems (2002)
Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu
Supervised Learning from Microarray Data. COMPSTAT (2002)
Trevor Hastie, Robert Tibshirani
Independent Components Analysis through Product Density Estimation. NIPS (2002)
Olga G. Troyanskaya, Michael N. Cantor, Gavin Sherlock, Patrick O. Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman
Missing value estimation methods for DNA microarrays. Bioinform. 17 (6) (2001)
Ji Zhu, Trevor Hastie
Kernel Logistic Regression and the Import Vector Machine. NIPS (2001)
Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black, Trevor Hastie
Learning and Tracking Cyclic Human Motion. NIPS (2000)
Dirk Ormoneit, Trevor Hastie
Optimal Kernel Shapes for Local Linear Regression. NIPS (1999)
Thomas D. Wu, Scott C. Schmidler, Trevor Hastie, Douglas L. Brutlag
Regression Analysis of Multiple Protein Structures. J. Comput. Biol. 5 (3) (1998)
Thomas D. Wu, Trevor Hastie, Scott C. Schmidler, Douglas L. Brutlag
Regression analysis of multiple protein structures. RECOMB (1998)
Gareth James, Trevor Hastie
The Error Coding and Substitution PaCTs. NIPS (1997)
Y. Dan Rubinstein, Trevor Hastie
Discriminative vs Informative Learning. KDD (1997)
Trevor Hastie, Robert Tibshirani
Classification by Pairwise Coupling. NIPS (1997)
Trevor Hastie, Robert Tibshirani
Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. 18 (6) (1996)
Trevor Hastie, Robert Tibshirani
Discriminant Adaptive Nearest Neighbor Classification and Regression. NIPS (1995)
Trevor Hastie, Robert Tibshirani
Discriminant Adaptive Nearest Neighbor Classification. KDD (1995)
Trevor Hastie, Patrice Y. Simard
Learning Prototype Models for Tangent Distance. NIPS (1994)
Winston Nelson, William Turin, Trevor Hastie
Statistical Methods for On-Line Signature Verification. Int. J. Pattern Recognit. Artif. Intell. 8 (3) (1994)
Eyal Kishon, Trevor Hastie, Haim J. Wolfson
3-D curve matching using splines. J. Field Robotics 8 (6) (1991)
Eyal Kishon, Trevor Hastie, Haim J. Wolfson
3-D Curve Matching Using Splines. ECCV (1990)