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ORCIDTrevor J. Hastie
Trevor Hastie
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- affiliation: Stanford University, Department of Statistics
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2020 – today
- 2024
[j36]Lukasz Kidzinski
, Trevor Hastie:
Modeling Longitudinal Data Using Matrix Completion. J. Comput. Graph. Stat. 33(2): 551-566 (2024)- [i19]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 abs/2404.17626 (2024) - [i18]James Yang, Trevor Hastie:
A Fast and Scalable Pathwise-Solver for Group Lasso and Elastic Net Penalized Regression via Block-Coordinate Descent. CoRR abs/2405.08631 (2024) - [i17]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 abs/2406.08322 (2024) - [i16]Disha Ghandwani, Trevor Hastie:
Scalable recommender system based on factor analysis. CoRR abs/2408.05896 (2024) - [i15]Tetiana Parshakova, Trevor Hastie, Stephen Boyd:
Fitting Multilevel Factor Models. CoRR abs/2409.12067 (2024) - 2023
- [j35]J. Kenneth Tay, Balasubramanian Narasimhan, Trevor Hastie:
Elastic Net Regularization Paths for All Generalized Linear Models. J. Stat. Softw. 106(1) (2023) - [i14]Anav Sood, Trevor Hastie:
A Statistical View of Column Subset Selection. CoRR abs/2307.12892 (2023) - [i13]Tetiana Parshakova, Trevor Hastie, Eric Darve, Stephen Boyd:
Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices. CoRR abs/2310.19214 (2023) - 2022
- [j34]Didier Nibbering, Trevor J. Hastie:
Multiclass-penalized logistic regression. Comput. Stat. Data Anal. 169: 107414 (2022) - [j33]Jeremy Jenrette, Zac Yung-Chun Liu, P. Chimote, Trevor Hastie, E. Fox, F. Ferretti:
Shark detection and classification with machine learning. Ecol. Informatics 69: 101673 (2022) - [j32]Zijun Gao, Trevor Hastie:
LinCDE: Conditional Density Estimation via Lindsey's Method. J. Mach. Learn. Res. 23: 52:1-52:55 (2022) - [j31]Lukasz Kidzinski, Francis K. C. Hui, David I. Warton, Trevor J. Hastie:
Generalized Matrix Factorization: efficient algorithms for fitting generalized linear latent variable models to large data arrays. J. Mach. Learn. Res. 23: 291:1-291:29 (2022) - [i12]Ismael Lemhadri, Harrison H. Li, Trevor Hastie:
RbX: Region-based explanations of prediction models. CoRR abs/2210.08721 (2022) - 2021
- [j30]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): 4148-4155 (2021) - [j29]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): 4437-4443 (2021) - [j28]Leonardo Tozzi, Elena Tuzhilina, Matthew F. Glasser, Trevor J. Hastie, Leanne M. Williams:
Relating whole-brain functional connectivity to self-reported negative emotion in a large sample of young adults using group regularized canonical correlation analysis. NeuroImage 237: 118137 (2021) - [i11]Elena Tuzhilina, Trevor Hastie:
Weighted Low Rank Matrix Approximation and Acceleration. CoRR abs/2109.11057 (2021) - 2020
- [j27]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): 619-628 (2020) - [j26]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:
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. NeuroImage 214: 116715 (2020) - [j25]Trevor Hastie:
Ridge Regularization: An Essential Concept in Data Science. Technometrics 62(4): 426-433 (2020) - [i10]Trevor Hastie:
Ridge Regularizaton: an Essential Concept in Data Science. CoRR abs/2006.00371 (2020) - [i9]J. Kenneth Tay, Nima Aghaeepour, Trevor Hastie, Robert Tibshirani:
Feature-weighted elastic net: using "features of features" for better prediction. CoRR abs/2006.01395 (2020) - [i8]Yifang Liu, Zhentao Xu, Qiyuan An, Yang Yi, Yanzhi Wang, Trevor Hastie:
Simultaneous Relevance and Diversity: A New Recommendation Inference Approach. CoRR abs/2009.12969 (2020) - [i7]Lukasz Kidzinski, Francis K. C. Hui, David I. Warton, Trevor Hastie:
Generalized Matrix Factorization. CoRR abs/2010.02469 (2020)
2010 – 2019
- 2019
- [i6]Trevor Hastie, Andrea Montanari, Saharon Rosset, Ryan J. Tibshirani:
Surprises in High-Dimensional Ridgeless Least Squares Interpolation. CoRR abs/1903.08560 (2019) - 2018
- [i5]Lukasz Kidzinski, Trevor Hastie:
Longitudinal data analysis using matrix completion. CoRR abs/1809.08771 (2018) - 2017
- [j24]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:
FIRE: functional inference of genetic variants that regulate gene expression. Bioinform. 33(24): 3895-3901 (2017) - [j23]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): 565-576 (2017) - [j22]Nicholas Boyd, Trevor Hastie, Stephen P. Boyd, Benjamin Recht, Michael I. Jordan:
Saturating Splines and Feature Selection. J. Mach. Learn. Res. 18: 197:1-197:32 (2017) - 2016
- [c32]Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie:
Data Representation and Compression Using Linear-Programming Approximations. ICLR (Poster) 2016 - 2015
- [j21]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): 1120-1125 (2015) - [j20]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: 3367-3402 (2015) - [c31]Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie:
Fast Algorithms for Learning with Long N-grams via Suffix Tree Based Matrix Multiplication. UAI 2015: 672-681 - [i4]Rakesh Achanta, Trevor Hastie:
Telugu OCR Framework using Deep Learning. CoRR abs/1509.05962 (2015) - 2014
- [j19]Stefan Wager, Trevor Hastie, Bradley Efron:
Confidence intervals for random forests: the jackknife and the infinitesimal jackknife. J. Mach. Learn. Res. 15(1): 1625-1651 (2014) - [c30]Hristo S. Paskov, John C. Mitchell, Trevor J. Hastie:
An Efficient Algorithm for Large Scale Compressive Feature Learning. AISTATS 2014: 760-768 - 2013
- [c29]Jason D. Lee, Trevor Hastie:
Structure Learning of Mixed Graphical Models. AISTATS 2013: 388-396 - [c28]Hristo S. Paskov, Robert West, John C. Mitchell, Trevor J. Hastie:
Compressive Feature Learning. NIPS 2013: 2931-2939 - 2012
- [j18]Rahul Mazumder, Trevor Hastie:
Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso. J. Mach. Learn. Res. 13: 781-794 (2012) - [i3]Jason D. Lee, Trevor Hastie:
Learning Mixed Graphical Models. CoRR abs/1205.5012 (2012) - 2011
- [j17]Line H. Clemmensen, Trevor Hastie, Daniela M. Witten, Bjarne K. Ersbøll:
Sparse Discriminant Analysis. Technometrics 53(4): 406-413 (2011) - [i2]Rahul Mazumder, Trevor Hastie:
The Graphical Lasso: New Insights and Alternatives. CoRR abs/1111.5479 (2011) - 2010
- [j16]Rahul Mazumder, Trevor Hastie, Robert Tibshirani:
Spectral Regularization Algorithms for Learning Large Incomplete Matrices. J. Mach. Learn. Res. 11: 2287-2322 (2010) - [j15]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) - [j14]Michael Greenacre, Trevor Hastie:
Dynamic visualization of statistical learning in the context of high-dimensional textual data. J. Web Semant. 8(2-3): 163-168 (2010)
2000 – 2009
- 2009
- [b2]Trevor Hastie, Robert Tibshirani, Jerome H. Friedman:
The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. Springer Series in Statistics, Springer 2009, ISBN 9780387848570, pp. I-XXII, 1-745 - [j13]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): 714-721 (2009) - 2008
- [c27]Trevor Hastie, Jerome H. Friedman, Robert Tibshirani:
Regularization paths and coordinate descent. KDD 2008: 3 - [c26]Ping Li, Kenneth Ward Church, Trevor Hastie:
One sketch for all: Theory and Application of Conditional Random Sampling. NIPS 2008: 953-960 - 2007
- [j12]Robert Tibshirani, Trevor Hastie:
Margin Trees for High-dimensional Classification. J. Mach. Learn. Res. 8: 637-652 (2007) - [j11]Ping Li, Trevor Hastie, Kenneth Ward Church:
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections. J. Mach. Learn. Res. 8: 2497-2532 (2007) - [c25]Ping Li, Trevor Hastie, Kenneth Ward Church:
Nonlinear Estimators and Tail Bounds for Dimension Reduction in l 1 Using Cauchy Random Projections. COLT 2007: 514-529 - [c24]Ping Li, Trevor Hastie:
A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections. NIPS 2007: 905-912 - 2006
- [c23]Ping Li, Trevor Hastie, Kenneth Ward Church:
Improving Random Projections Using Marginal Information. COLT 2006: 635-649 - [c22]Ping Li, Trevor Hastie, Kenneth Ward Church:
Very sparse random projections. KDD 2006: 287-296 - [c21]Ping Li, Kenneth Ward Church, Trevor Hastie:
Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data. NIPS 2006: 873-880 - [i1]Ping Li, Trevor Hastie, Kenneth Ward Church:
Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$ Using Cauchy Random Projections. CoRR abs/cs/0610155 (2006) - 2005
- [j10]Dirk Ormoneit, Michael J. Black, Trevor Hastie, Hedvig Kjellström:
Representing cyclic human motion using functional analysis. Image Vis. Comput. 23(14): 1264-1276 (2005) - 2004
- [j9]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): 3034-3044 (2004) - [j8]Saharon Rosset, Ji Zhu, Trevor Hastie:
Boosting as a Regularized Path to a Maximum Margin Classifier. J. Mach. Learn. Res. 5: 941-973 (2004) - [j7]Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu:
The Entire Regularization Path for the Support Vector Machine. J. Mach. Learn. Res. 5: 1391-1415 (2004) - [c20]Philip Beineke, Trevor Hastie, Shivakumar Vaithyanathan:
The Sentimental Factor: Improving Review Classification Via Human-Provided Information. ACL 2004: 263-270 - [c19]Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu:
The Entire Regularization Path for the Support Vector Machine. NIPS 2004: 561-568 - [c18]Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie:
A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning. NIPS 2004: 1161-1168 - 2003
- [j6]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): 1477-1480 (2003) - [c17]Saharon Rosset, Ji Zhu, Trevor Hastie:
Boosting and support vector machines as optimal separators. DRR 2003: 1-7 - [c16]Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani:
1-norm Support Vector Machines. NIPS 2003: 49-56 - [c15]Saharon Rosset, Ji Zhu, Trevor Hastie:
Margin Maximizing Loss Functions. NIPS 2003: 1237-1244 - 2002
- [c14]Trevor Hastie, Robert Tibshirani, Balasubramanian Narasimhan, Gilbert Chu:
Supervised Learning from Microarray Data. COMPSTAT 2002: 67-77 - [c13]Ji Zhu, Trevor Hastie:
Support Vector Machines, Kernel Logistic Regression and Boosting. Multiple Classifier Systems 2002: 16-26 - [c12]Trevor Hastie, Robert Tibshirani:
Independent Components Analysis through Product Density Estimation. NIPS 2002: 649-656 - 2001
- [b1]Trevor Hastie, Jerome H. Friedman, Robert Tibshirani:
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer Series in Statistics, Springer 2001, ISBN 978-1-4899-0519-2, pp. 1-536 - [j5]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): 520-525 (2001) - [c11]Ji Zhu, Trevor Hastie:
Kernel Logistic Regression and the Import Vector Machine. NIPS 2001: 1081-1088 - 2000
- [c10]Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black, Trevor Hastie:
Learning and Tracking Cyclic Human Motion. NIPS 2000: 894-900
1990 – 1999
- 1999
- [c9]Dirk Ormoneit, Trevor Hastie:
Optimal Kernel Shapes for Local Linear Regression. NIPS 1999: 540-546 - 1998
- [j4]Thomas D. Wu, Scott C. Schmidler, Trevor Hastie, Douglas L. Brutlag:
Regression Analysis of Multiple Protein Structures. J. Comput. Biol. 5(3): 585-595 (1998) - [c8]Thomas D. Wu, Trevor Hastie, Scott C. Schmidler, Douglas L. Brutlag:
Regression analysis of multiple protein structures. RECOMB 1998: 276-284 - 1997
- [c7]Y. Dan Rubinstein, Trevor Hastie:
Discriminative vs Informative Learning. KDD 1997: 49-53 - [c6]Trevor Hastie, Robert Tibshirani:
Classification by Pairwise Coupling. NIPS 1997: 507-513 - [c5]Gareth James, Trevor Hastie:
The Error Coding and Substitution PaCTs. NIPS 1997: 542-548 - 1996
- [j3]Trevor Hastie, Robert Tibshirani:
Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 607-616 (1996) - 1995
- [c4]Trevor Hastie, Robert Tibshirani:
Discriminant Adaptive Nearest Neighbor Classification. KDD 1995: 142-149 - [c3]Trevor Hastie, Robert Tibshirani:
Discriminant Adaptive Nearest Neighbor Classification and Regression. NIPS 1995: 409-415 - 1994
- [j2]Winston L. Nelson, William Turin, Trevor Hastie:
Statistical Methods for On-Line Signature Verification. Int. J. Pattern Recognit. Artif. Intell. 8(3): 749-770 (1994) - [c2]Trevor Hastie, Patrice Y. Simard:
Learning Prototype Models for Tangent Distance. NIPS 1994: 999-1006 - 1991
- [j1]Eyal Kishon, Trevor Hastie, Haim J. Wolfson:
3-D curve matching using splines. J. Field Robotics 8(6): 723-743 (1991) - 1990
- [c1]Eyal Kishon, Trevor Hastie, Haim J. Wolfson:
3-D Curve Matching Using Splines. ECCV 1990: 589-591
Coauthor Index
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