3-D curve matching using splines. - Eyal Kishon; Trevor Hastie; Haim J. Wolfson (J. Field Robotics, 1991)

Learning Prototype Models for Tangent Distance. - Trevor Hastie; Patrice Y. Simard (NIPS, 1994)

Statistical Methods for On-Line Signature Verification. - Winston L. Nelson; William Turin; Trevor Hastie (Int. J. Pattern Recognit. Artif. Intell., 1994)

Discriminant Adaptive Nearest Neighbor Classification and Regression. - Trevor Hastie; Robert Tibshirani (NIPS, 1995)

Discriminant Adaptive Nearest Neighbor Classification. - Trevor Hastie; Robert Tibshirani (IEEE Trans. Pattern Anal. Mach. Intell., 1996)

Discriminative vs Informative Learning. - Y. Dan Rubinstein; Trevor Hastie (KDD, 1997)

Classification by Pairwise Coupling. - Trevor Hastie; Robert Tibshirani (NIPS, 1997)

The Error Coding and Substitution PaCTs. - Gareth James; Trevor Hastie (NIPS, 1997)

Regression Analysis of Multiple Protein Structures. - Thomas D. Wu; Scott C. Schmidler; Trevor Hastie; Douglas L. Brutlag (J. Comput. Biol., 1998)

Optimal Kernel Shapes for Local Linear Regression. - Dirk Ormoneit; Trevor Hastie (NIPS, 1999)

Learning and Tracking Cyclic Human Motion. - Dirk Ormoneit; Hedvig Sidenbladh; Michael J. Black; Trevor Hastie (NIPS, 2000)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction - Trevor Hastie; Jerome H. Friedman; Robert Tibshirani (Springer, 2001)

Kernel Logistic Regression and the Import Vector Machine. - Ji Zhu 001; Trevor Hastie (NIPS, 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)

Supervised Learning from Microarray Data. - Trevor Hastie; Robert Tibshirani; Balasubramanian Narasimhan; Gilbert Chu (COMPSTAT, 2002)

Support Vector Machines, Kernel Logistic Regression and Boosting. - Ji Zhu 001; Trevor Hastie (Multiple Classifier Systems, 2002)

Independent Components Analysis through Product Density Estimation. - Trevor Hastie; Robert Tibshirani (NIPS, 2002)

Boosting and support vector machines as optimal separators. - Saharon Rosset; Ji Zhu 001; Trevor Hastie (DRR, 2003)

Margin Maximizing Loss Functions. - Saharon Rosset; Ji Zhu 001; Trevor Hastie (NIPS, 2003)

1-norm Support Vector Machines. - Ji Zhu 001; Saharon Rosset; Trevor Hastie; Robert Tibshirani (NIPS, 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)

The Sentimental Factor: Improving Review Classification Via Human-Provided Information. - Philip Beineke; Trevor Hastie; Shivakumar Vaithyanathan (ACL, 2004)

A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning. - Saharon Rosset; Ji Zhu 001; Hui Zou; Trevor Hastie (NIPS, 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)

The Entire Regularization Path for the Support Vector Machine. - Trevor Hastie; Saharon Rosset; Robert Tibshirani; Ji Zhu 001 (J. Mach. Learn. Res., 2004)

Boosting as a Regularized Path to a Maximum Margin Classifier. - Saharon Rosset; Ji Zhu 001; Trevor Hastie (J. Mach. Learn. Res., 2004)

Representing cyclic human motion using functional analysis. - Dirk Ormoneit; Michael J. Black; Trevor Hastie; Hedvig Kjellström (Image Vis. Comput., 2005)

Improving Random Projections Using Marginal Information. - Ping Li 001; Trevor Hastie; Kenneth Ward Church (COLT, 2006)

Very sparse random projections. - Ping Li 001; Trevor Hastie; Kenneth Ward Church (KDD, 2006)

Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data. - Ping Li 001; Kenneth Ward Church; Trevor Hastie (NIPS, 2006)

Nonlinear Estimators and Tail Bounds for Dimension Reduction in $l_1$ Using Cauchy Random Projections - Ping Li 001; Trevor Hastie; Kenneth Ward Church (CoRR, 2006)

A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections. - Ping Li 001; Trevor Hastie (NIPS, 2007)

Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections. - Ping Li 001; Trevor Hastie; Kenneth Ward Church (J. Mach. Learn. Res., 2007)

Margin Trees for High-dimensional Classification. - Robert Tibshirani; Trevor Hastie (J. Mach. Learn. Res., 2007)

Regularization paths and coordinate descent. - Trevor Hastie; Jerome H. Friedman; Robert Tibshirani (KDD, 2008)

One sketch for all: Theory and Application of Conditional Random Sampling. - Ping Li 001; Kenneth Ward Church; Trevor Hastie (NIPS, 2008)

The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd Edition. - Trevor Hastie; Robert Tibshirani; Jerome H. Friedman (Springer, 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)

Spectral Regularization Algorithms for Learning Large Incomplete Matrices. - Rahul Mazumder; Trevor Hastie; Robert Tibshirani (J. Mach. Learn. Res., 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 006; Trevor Hastie; Atul J. Butte (PLoS Comput. Biol., 2010)

Dynamic visualization of statistical learning in the context of high-dimensional textual data. - Michael Greenacre; Trevor Hastie (J. Web Semant., 2010)

The Graphical Lasso: New Insights and Alternatives - Rahul Mazumder; Trevor Hastie (CoRR, 2011)

Sparse Discriminant Analysis. - Line H. Clemmensen; Trevor Hastie; Daniela M. Witten; Bjarne K. Ersbøll (Technometrics, 2011)

Learning Mixed Graphical Models - Jason D. Lee; Trevor Hastie (CoRR, 2012)

Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso. - Rahul Mazumder; Trevor Hastie (J. Mach. Learn. Res., 2012)

Structure Learning of Mixed Graphical Models. - Jason D. Lee; Trevor Hastie (AISTATS, 2013)

Compressive Feature Learning. - Hristo S. Paskov; Robert West 001; John C. Mitchell; Trevor J. Hastie (NIPS, 2013)

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

Confidence intervals for random forests: the jackknife and the infinitesimal jackknife. - Stefan Wager; Trevor Hastie; Bradley Efron (J. Mach. Learn. Res., 2014)

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

Telugu OCR Framework using Deep Learning. - Rakesh Achanta; Trevor Hastie (CoRR, 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)

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)

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

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; et al. (Bioinform., 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; et al. (J. Am. Medical Informatics Assoc., 2017)

Saturating Splines and Feature Selection. - Nicholas Boyd; Trevor Hastie; Stephen P. Boyd; Benjamin Recht; Michael I. Jordan (J. Mach. Learn. Res., 2017)

Longitudinal data analysis using matrix completion. - Lukasz Kidzinski; Trevor Hastie (CoRR, 2018)

Surprises in High-Dimensional Ridgeless Least Squares Interpolation. - Trevor Hastie; Andrea Montanari; Saharon Rosset; Ryan J. Tibshirani (CoRR, 2019)

Ridge Regularizaton: an Essential Concept in Data Science. - 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)

Simultaneous Relevance and Diversity: A New Recommendation Inference Approach. - Yifang Liu; Zhentao Xu; Qiyuan An; Yang Yi 002; Yanzhi Wang; Trevor Hastie (CoRR, 2020)

Generalized Matrix Factorization. - Lukasz Kidzinski; Francis K. C. Hui; David I. Warton; Trevor Hastie (CoRR, 2020)

Integration of mechanistic immunological knowledge into a machine learning pipeline improves predictions. - Anthony Culos; Amy Tsai; Natalie Stanley; Martin Becker 003; Mohammad Sajjad Ghaemi; David Mcilwain; Ramin Fallahzadeh; Athena Tanada; Huda Nassar; Camilo Espinosa; Maria Xenochristou; Edward Ganio; et al. (Nat. Mach. Intell., 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; et al. (NeuroImage, 2020)

Ridge Regularization: An Essential Concept in Data Science. - Trevor Hastie (Technometrics, 2020)

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)

Weighted Low Rank Matrix Approximation and Acceleration. - Elena Tuzhilina; Trevor Hastie (CoRR, 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)

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., 2021)

RbX: Region-based explanations of prediction models. - Ismael Lemhadri; Harrison H. Li; Trevor Hastie (CoRR, 2022)

Multiclass-penalized logistic regression. - Didier Nibbering; Trevor J. Hastie (Comput. Stat. Data Anal., 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)

LinCDE: Conditional Density Estimation via Lindsey's Method. - Zijun Gao; Trevor Hastie (J. Mach. Learn. Res., 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)

A Statistical View of Column Subset Selection. - Anav Sood; Trevor Hastie (CoRR, 2023)

Factor Fitting, Rank Allocation, and Partitioning in Multilevel Low Rank Matrices. - Tetiana Parshakova; Trevor Hastie; Eric Darve; Stephen Boyd (CoRR, 2023)

Elastic Net Regularization Paths for All Generalized Linear Models. - J. Kenneth Tay; Balasubramanian Narasimhan; Trevor Hastie (J. Stat. Softw., 2023)

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)

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

Scalable recommender system based on factor analysis. - Disha Ghandwani; Trevor Hastie (CoRR, 2024)

Fitting Multilevel Factor Models. - Tetiana Parshakova; Trevor Hastie; Stephen Boyd (CoRR, 2024)

Modeling Longitudinal Data Using Matrix Completion. - Lukasz Kidzinski; Trevor Hastie (J. Comput. Graph. Stat., 2024)

Smooth Multi-Period Forecasting With Application to Prediction of COVID-19 Cases. - Elena Tuzhilina; Trevor J. Hastie; Daniel J. McDonald; J. Kenneth Tay; Robert Tibshirani (J. Comput. Graph. Stat., 2024)

A randomized clinical trial testing digital mindset intervention for knee osteoarthritis pain and activity improvement. - Melissa A. Boswell; Kris M. Evans; Disha Ghandwani; Trevor Hastie; Sean R. Zion; Paula Moya; Nicholas J. Giori; Jennifer L. Hicks; Alia Crum; Scott L. Delp (npj Digit. Medicine, 2024)

Fitting Multilevel Factor Models. - Tetiana Parshakova; Trevor Hastie; Stephen P. Boyd (SIAM J. Matrix Anal. Appl., 2025)

ExpFamilyPCA.jl: A Julia Package for Exponential Family Principal Component Analysis. - Logan Mondal Bhamidipaty; Mykel J. Kochenderfer; Trevor Hastie (Zenodo, 2025)

ExpFamilyPCA.jl: A Julia Package for Exponential Family Principal Component Analysis. - Logan Mondal Bhamidipaty; Mykel J. Kochenderfer; Trevor Hastie (J. Open Source Softw., 2025)

Note on the Equivalence of Orthogonalizing EM and Proximal Gradient Descent. - James Yang; Trevor Hastie (Technometrics, 2025)

Non-smooth optimization meets automated material model discovery. - Moritz Flaschel; Trevor Hastie; Ellen Kuhl (CoRR, 2025)