Trevor J. Hastie

According to our database, Trevor J. Hastie authored at least 84 papers between 1991 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Modeling Longitudinal Data Using Matrix Completion.

Lukasz Kidzinski, Trevor Hastie

J. Comput. Graph. Stat., 2024

Fitting Multilevel Factor Models.

Tetiana Parshakova, Trevor Hastie, Stephen Boyd

CoRR, 2024

Scalable recommender system based on factor analysis.

Disha Ghandwani, Trevor Hastie

CoRR, 2024

MMIL: A novel algorithm for disease associated cell type discovery.

Erin Craig, Timothy Keyes, Jolanda Sarno, Maxim Zaslavsky, Garry Nolan, Kara Davis, Trevor Hastie, Robert Tibshirani

CoRR, 2024

A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent.

James Yang, Trevor Hastie

CoRR, 2024

Using Pre-training and Interaction Modeling for ancestry-specific disease prediction in UK Biobank.

Thomas Le Menestrel, Erin Craig, Robert Tibshirani, Trevor Hastie, Manuel A. Rivas

CoRR, 2024

2023

Elastic Net Regularization Paths for All Generalized Linear Models.

J. Kenneth Tay, Balasubramanian Narasimhan, Trevor Hastie

J. Stat. Softw., 2023

Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices.

Tetiana Parshakova, Trevor Hastie, Eric Darve, Stephen Boyd

CoRR, 2023

A Statistical View of Column Subset Selection.

Anav Sood, Trevor Hastie

CoRR, 2023

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

J. Mach. Learn. Res., 2022

LinCDE: Conditional Density Estimation via Lindsey's Method.

Zijun Gao, Trevor Hastie

J. Mach. Learn. Res., 2022

Shark detection and classification with machine learning.

Jeremy Jenrette, Zac Yung-Chun Liu, P. Chimote, Trevor Hastie, E. Fox, F. Ferretti

Ecol. Informatics, 2022

Multiclass-penalized logistic regression.

Didier Nibbering, Trevor J. Hastie

Comput. Stat. Data Anal., 2022

RbX: Region-based explanations of prediction models.

Ismael Lemhadri, Harrison H. Li, Trevor Hastie

CoRR, 2022

2021

Fast numerical optimization for genome sequencing data in population biobanks.

Ruilin Li, Christopher Chang, Yosuke Tanigawa, Balasubramanian Narasimhan, Trevor Hastie, Robert Tibshirani, Manuel A. Rivas

Bioinform., November, 2021

Relating whole-brain functional connectivity to self-reported negative emotion in a large sample of young adults using group regularized canonical correlation analysis.

Leonardo Tozzi, Elena Tuzhilina, Matthew F. Glasser, Trevor J. Hastie, Leanne M. Williams

NeuroImage, 2021

Weighted Low Rank Matrix Approximation and Acceleration.

Elena Tuzhilina, Trevor Hastie

CoRR, 2021

Survival analysis on rare events using group-regularized multi-response Cox regression.

Ruilin Li, Yosuke Tanigawa, Johanne M. Justesen, Jonathan Taylor, Trevor Hastie, Robert Tibshirani, Manuel A. Rivas

Bioinform., 2021

2020

Ridge Regularization: An Essential Concept in Data Science.

Trevor Hastie

Technometrics, 2020

The human connectome project for disordered emotional states: Protocol and rationale for a research domain criteria study of brain connectivity in young adult anxiety and depression.

Leonardo Tozzi, Brooke Staveland, Bailey Holt-Gosselin, Megan Chesnut, Sarah E. Chang, David Choi, Melissa Shiner, Hua Wu, Garikoitz Lerma-Usabiaga, Olaf Sporns, Deanna M. Barch, Ian H. Gotlib, Trevor J. Hastie, Adam B. Kerr, Russell A. Poldrack, Brian A. Wandell, Max Wintermark, Leanne M. Williams

NeuroImage, 2020

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions.

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

Nat. Mach. Intell., 2020

Generalized Matrix Factorization.

Lukasz Kidzinski, Francis K. C. Hui, David I. Warton, Trevor Hastie

CoRR, 2020

Simultaneous Relevance and Diversity: A New Recommendation Inference Approach.

Yifang Liu, Zhentao Xu, Qiyuan An, Yang Yi, Yanzhi Wang, Trevor Hastie

CoRR, 2020

Feature-weighted elastic net: using "features of features" for better prediction.

J. Kenneth Tay, Nima Aghaeepour, Trevor Hastie, Robert Tibshirani

CoRR, 2020

Ridge Regularizaton: an Essential Concept in Data Science.

Trevor Hastie

CoRR, 2020

2019

Surprises in High-Dimensional Ridgeless Least Squares Interpolation.

Trevor Hastie, Andrea Montanari, Saharon Rosset, Ryan J. Tibshirani

CoRR, 2019

2018

Longitudinal data analysis using matrix completion.

Lukasz Kidzinski, Trevor Hastie

CoRR, 2018

2017

Saturating Splines and Feature Selection.

Nicholas Boyd, Trevor Hastie, Stephen P. Boyd, Benjamin Recht, Michael I. Jordan

J. Mach. Learn. Res., 2017

Synergistic drug combinations from electronic health records and gene expression.

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

J. Am. Medical Informatics Assoc., 2017

FIRE: functional inference of genetic variants that regulate gene expression.

Nilah M. Ioannidis, Joe R. Davis, Marianne K. DeGorter, Nicholas B. Larson, Shannon K. McDonnell, Amy J. French, Alexis J. Battle, Trevor J. Hastie, Stephen N. Thibodeau, Stephen B. Montgomery, Carlos D. Bustamante, Weiva Sieh, Alice S. Whittemore

Bioinform., 2017

2016

Data Representation and Compression Using Linear-Programming Approximations.

Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie

Proceedings of the 4th International Conference on Learning Representations, 2016

2015

Matrix completion and low-rank SVD via fast alternating least squares.

Trevor Hastie, Rahul Mazumder, Jason D. Lee, Reza Zadeh

J. Mach. Learn. Res., 2015

The mobilize center: an NIH big data to knowledge center to advance human movement research and improve mobility.

Joy P. Ku, Jennifer L. Hicks, Trevor Hastie, Jure Leskovec, Christopher Ré, Scott L. Delp

J. Am. Medical Informatics Assoc., 2015

Telugu OCR Framework using Deep Learning.

Rakesh Achanta, Trevor Hastie

CoRR, 2015

Fast Algorithms for Learning with Long N-grams via Suffix Tree Based Matrix Multiplication.

Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie

Proceedings of the Thirty-First Conference on Uncertainty in Artificial Intelligence, 2015

2014

Confidence intervals for random forests: the jackknife and the infinitesimal jackknife.

Stefan Wager, Trevor Hastie, Bradley Efron

J. Mach. Learn. Res., 2014

An Efficient Algorithm for Large Scale Compressive Feature Learning.

Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

Compressive Feature Learning.

Hristo S. Paskov, Robert West, John C. Mitchell, Trevor J. Hastie

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Structure Learning of Mixed Graphical Models.

Jason D. Lee, Trevor Hastie

Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics, 2013

2012

Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso.

Rahul Mazumder, Trevor Hastie

J. Mach. Learn. Res., 2012

Learning Mixed Graphical Models

Jason D. Lee, Trevor Hastie

CoRR, 2012

2011

Sparse Discriminant Analysis.

Line H. Clemmensen, Trevor Hastie, Daniela M. Witten, Bjarne K. Ersbøll

Technometrics, 2011

The Graphical Lasso: New Insights and Alternatives

Rahul Mazumder, Trevor Hastie

CoRR, 2011

2010

Dynamic visualization of statistical learning in the context of high-dimensional textual data.

Michael Greenacre, Trevor Hastie

J. Web Semant., 2010

Network-Based Elucidation of Human Disease Similarities Reveals Common Functional Modules Enriched for Pluripotent Drug Targets.

Silpa Suthram, Joel Dudley, Annie P. Chiang, Rong Chen, Trevor Hastie, Atul J. Butte

PLoS Comput. Biol., 2010

Spectral Regularization Algorithms for Learning Large Incomplete Matrices.

Rahul Mazumder, Trevor Hastie, Robert Tibshirani

J. Mach. Learn. Res., 2010

2009

Genome-wide association analysis by lasso penalized logistic regression.

Tong Tong Wu, Yi Fang Chen, Trevor Hastie, Eric M. Sobel, Kenneth Lange

Bioinform., 2009

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition.

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman

Springer Series in Statistics, Springer, ISBN: 9780387848570, 2009

2008

One sketch for all: Theory and Application of Conditional Random Sampling.

Ping Li, Kenneth Ward Church, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 21, 2008

Regularization paths and coordinate descent.

Trevor Hastie, Jerome H. Friedman, Robert Tibshirani

Proceedings of the 14th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2008

2007

Margin Trees for High-dimensional Classification.

Robert Tibshirani, Trevor Hastie

J. Mach. Learn. Res., 2007

Nonlinear Estimators and Tail Bounds for Dimension Reduction in <i>l</i>₁ Using Cauchy Random Projections.

Ping Li, Trevor Hastie, Kenneth Ward Church

J. Mach. Learn. Res., 2007

A Unified Near-Optimal Estimator For Dimension Reduction in l_alpha(0 < alpha <= 2) Using Stable Random Projections.

Ping Li, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 20, 2007

2006

Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$ Using Cauchy Random Projections

Ping Li, Trevor Hastie, Kenneth Ward Church

CoRR, 2006

Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data.

Ping Li, Kenneth Ward Church, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 19, 2006

Very sparse random projections.

Ping Li, Trevor Hastie, Kenneth Ward Church

Proceedings of the Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2006

Improving Random Projections Using Marginal Information.

Ping Li, Trevor Hastie, Kenneth Ward Church

Proceedings of the Learning Theory, 19th Annual Conference on Learning Theory, 2006

2005

Representing cyclic human motion using functional analysis.

Dirk Ormoneit, Michael J. Black, Trevor Hastie, Hedvig Kjellström

Image Vis. Comput., 2005

2004

Boosting as a Regularized Path to a Maximum Margin Classifier.

Saharon Rosset, Ji Zhu, Trevor Hastie

J. Mach. Learn. Res., 2004

The Entire Regularization Path for the Support Vector Machine.

Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu

J. Mach. Learn. Res., 2004

Sample classification from protein mass spectrometry, by 'peak probability contrasts'.

Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, Scott G. Soltys, Gongyi Shi, Albert C. Koong, Quynh-Thu Le

Bioinform., 2004

A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning.

Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 17 [Neural Information Processing Systems, 2004

The Sentimental Factor: Improving Review Classification Via Human-Provided Information.

Philip Beineke, Trevor Hastie, Shivakumar Vaithyanathan

Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics, 2004

2003

Note on "Comparison of Model Selection for Regression" by Vladimir Cherkassky and Yunqian Ma.

Trevor Hastie, Robert Tibshirani, Jerome H. Friedman

Neural Comput., 2003

1-norm Support Vector Machines.

Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Margin Maximizing Loss Functions.

Saharon Rosset, Ji Zhu, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 16 [Neural Information Processing Systems, 2003

Boosting and support vector machines as optimal separators.

Saharon Rosset, Ji Zhu, Trevor Hastie

Proceedings of the Document Recognition and Retrieval X, 2003

2002

Independent Components Analysis through Product Density Estimation.

Trevor Hastie, Robert Tibshirani

Proceedings of the Advances in Neural Information Processing Systems 15 [Neural Information Processing Systems, 2002

Support Vector Machines, Kernel Logistic Regression and Boosting.

Ji Zhu, Trevor Hastie

Proceedings of the Multiple Classifier Systems, Third International Workshop, 2002

Supervised Learning from Microarray Data.

Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu

Proceedings of the COMPSTAT 2002, 2002

2001

Missing value estimation methods for DNA microarrays.

Olga G. Troyanskaya, Michael N. Cantor, Gavin Sherlock, Patrick O. Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman

Bioinform., 2001

Kernel Logistic Regression and the Import Vector Machine.

Ji Zhu, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 14 [Neural Information Processing Systems: Natural and Synthetic, 2001

The Elements of Statistical Learning: Data Mining, Inference, and Prediction

Trevor Hastie, Jerome H. Friedman, Robert Tibshirani

Springer Series in Statistics, Springer, ISBN: 978-0-387-21606-5, 2001

2000

Learning and Tracking Cyclic Human Motion.

Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 13, 2000

1999

Optimal Kernel Shapes for Local Linear Regression.

Dirk Ormoneit, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 12, [NIPS Conference, Denver, Colorado, USA, November 29, 1999

1998

Regression Analysis of Multiple Protein Structures.

Thomas D. Wu, Scott C. Schmidler, Trevor Hastie, Douglas L. Brutlag

J. Comput. Biol., 1998

1997

The Error Coding and Substitution PaCTs.

Gareth James, Trevor Hastie

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Classification by Pairwise Coupling.

Trevor Hastie, Robert Tibshirani

Proceedings of the Advances in Neural Information Processing Systems 10, 1997

Discriminative vs Informative Learning.

Y. Dan Rubinstein, Trevor Hastie

Proceedings of the Third International Conference on Knowledge Discovery and Data Mining (KDD-97), 1997

1996

Discriminant Adaptive Nearest Neighbor Classification.

Trevor Hastie, Robert Tibshirani

IEEE Trans. Pattern Anal. Mach. Intell., 1996

1995

Discriminant Adaptive Nearest Neighbor Classification and Regression.

Trevor Hastie, Robert Tibshirani

Proceedings of the Advances in Neural Information Processing Systems 8, 1995

1994

Statistical Methods for On-Line Signature Verification.

Winston L. Nelson, William Turin, Trevor Hastie

Int. J. Pattern Recognit. Artif. Intell., 1994

Learning Prototype Models for Tangent Distance.

Trevor Hastie, Patrice Y. Simard

Proceedings of the Advances in Neural Information Processing Systems 7, 1994

1991

3-D curve matching using splines.

Eyal Kishon, Trevor Hastie, Haim J. Wolfson

J. Field Robotics, 1991