Trevor J. Hastie

发表

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

Robert Tibshirani, Trevor J. Hastie, Balasubramanian Narasimhan, 2004, Bioinform..

Saturating Splines and Feature Selection

Stephen P. Boyd, Michael I. Jordan, Trevor J. Hastie, 2016, J. Mach. Learn. Res..

Support Vector Machines, Kernel Logistic Regression and Boosting

Ji Zhu, Trevor J. Hastie, 2002, Multiple Classifier Systems.

Boosting and Support Vector Machines as Optimal Separators

Ji Zhu, Trevor J. Hastie, Saharon Rosset, 2003, IS&T/SPIE Electronic Imaging.

Classification by Pairwise Coupling

Robert Tibshirani, Trevor J. Hastie, R. Tibshirani, 1997, NIPS.

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

Ji Zhu, Hui Zou, Trevor J. Hastie, 2004, NIPS.

Regression Analysis of Multiple Protein Structures

Trevor J. Hastie, Douglas L. Brutlag, Thomas D. Wu, 1998, J. Comput. Biol..

Telugu OCR Framework using Deep Learning pdf

Trevor J. Hastie, Rakesh Achanta, 2015, ArXiv.

The Error Coding and Substitution PaCTs

Trevor J. Hastie, Gareth James, 1997, NIPS.

Genome-wide association analysis by lasso penalized logistic regression

Trevor J. Hastie, Kenneth Lange, Eric M. Sobel, 2009, Bioinform..

Statistical Methods for On-Line Signature Verification

William Turin, Trevor J. Hastie, Winston Nelson, 1994, Int. J. Pattern Recognit. Artif. Intell..

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

John C. Mitchell, Trevor J. Hastie, Hristo S. Paskov, 2015, UAI.

Learning Prototype Models for Tangent Distance

Patrice Y. Simard, Trevor J. Hastie, T. Hastie, 1994, NIPS.

Discriminant Adaptive Nearest Neighbor Classification and Regression

Robert Tibshirani, Trevor J. Hastie, R. Tibshirani, 1995, NIPS.

3-D curve matching using splines

Trevor J. Hastie, Haim J. Wolfson, Eyal Kishon, 1990, J. Field Robotics.

Structure Learning of Mixed Graphical Models

Trevor J. Hastie, Jason D. Lee, T. Hastie, 2013, AISTATS.

Regression analysis of multiple protein structures

Trevor J. Hastie, Douglas L. Brutlag, Thomas D. Wu, 1998, RECOMB '98.

Very sparse random projections

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2006, KDD '06.

Data Representation and Compression Using Linear-Programming Approximations pdf

John C. Mitchell, Trevor J. Hastie, Hristo S. Paskov, 2015, ICLR.

Learning Mixed Graphical Models pdf

Trevor J. Hastie, Jason D. Lee, T. Hastie, 2012, ArXiv.

Synergistic drug combinations from electronic health records and gene expression

Maya B. Mathur, Trevor J. Hastie, Nigam H. Shah, 2016, J. Am. Medical Informatics Assoc..

Regularization paths and coordinate descent

Robert Tibshirani, Trevor J. Hastie, Jerome H. Friedman, 2008, KDD.

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

Brian A. Wandell, Russell A. Poldrack, Olaf Sporns, 2020, NeuroImage.

Discriminant Adaptive Nearest Neighbor Classification

Robert Tibshirani, Trevor J. Hastie, R. Tibshirani, 1995, IEEE Trans. Pattern Anal. Mach. Intell..

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

Jure Leskovec, Christopher Ré, Trevor J. Hastie, 2015, J. Am. Medical Informatics Assoc..

Margin Maximizing Loss Functions

Ji Zhu, Trevor J. Hastie, Saharon Rosset, 2003, NIPS.

Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections pdf

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2006, J. Mach. Learn. Res..

Longitudinal data analysis using matrix completion pdf

Trevor J. Hastie, Lukasz Kidzinski, T. Hastie, 2018, ArXiv.

The Entire Regularization Path for the Support Vector Machine

Robert Tibshirani, Ji Zhu, Trevor J. Hastie, 2004, J. Mach. Learn. Res..

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

Trevor J. Hastie, Bradley Efron, Stefan Wager, 2013, J. Mach. Learn. Res..

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

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2008, NIPS.

An Efficient Algorithm for Large Scale Compressive Feature Learning

John C. Mitchell, Trevor J. Hastie, Hristo S. Paskov, 2014, AISTATS.

3-D curve matching using splines

Trevor J. Hastie, Haim J. Wolfson, Eyal Kishon, 1991, J. Field Robotics.

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

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2006, NIPS.

Representing cyclic human motion using functional analysis

Michael J. Black, Trevor J. Hastie, Hedvig Kjellström, 2005, Image Vis. Comput..

Learning and Tracking Cyclic Human Motion

Michael J. Black, Trevor J. Hastie, Hedvig Kjellström, 2000, NIPS.

Kernel Logistic Regression and the Import Vector Machine

Ji Zhu, Trevor J. Hastie, T. Hastie, 2001, NIPS.

Surprises in High-Dimensional Ridgeless Least Squares Interpolation pdf

Andrea Montanari, Trevor J. Hastie, Saharon Rosset, 2019, Annals of statistics.

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

Robert Tibshirani, Trevor J. Hastie, Jerome H. Friedman, 2001, Springer Series in Statistics.

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

Trevor J. Hastie, Shivakumar Vaithyanathan, Philip Beineke, 2004, ACL.

Spectral Regularization Algorithms for Learning Large Incomplete Matrices

Robert Tibshirani, Trevor J. Hastie, Rahul Mazumder, 2010, J. Mach. Learn. Res..

The Graphical Lasso: New Insights and Alternatives

Trevor J. Hastie, Rahul Mazumder, T. Hastie, 2011, Electronic journal of statistics.

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

Michael Greenacre, Trevor J. Hastie, 2010, J. Web Semant..

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

Trevor J. Hastie, Ping Li, T. Hastie, 2007, NIPS.

Discriminative vs Informative Learning

Trevor J. Hastie, Y. Dan Rubinstein, T. Hastie, 1997, KDD.

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

Robert Tibshirani, Trevor J. Hastie, Jerome H. Friedman, 2001, Springer Series in Statistics.

Margin Trees for High-dimensional Classification

Robert Tibshirani, Trevor J. Hastie, R. Tibshirani, 2007, J. Mach. Learn. Res..

Improving Random Projections Using Marginal Information

Kenneth Ward Church, Trevor J. Hastie, Ping Li, 2006, COLT.

FIRE: functional inference of genetic variants that regulate gene expression

Nicholas B. Larson, Trevor J. Hastie, Stephen B. Montgomery, 2017, Bioinform..

Compressive Feature Learning

John C. Mitchell, Trevor J. Hastie, Robert West, 2013, NIPS.

Optimal Kernel Shapes for Local Linear Regression

Trevor J. Hastie, Dirk Ormoneit, T. Hastie, 1999, NIPS.

Sparse Discriminant Analysis

Trevor J. Hastie, Daniela Witten, Line H. Clemmensen, 2011, Technometrics.

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

Robert Tibshirani, Trevor J. Hastie, Jerome H. Friedman, 2003, Neural Computation.

Boosting as a Regularized Path to a Maximum Margin Classifier

Ji Zhu, Trevor J. Hastie, Saharon Rosset, 2004, J. Mach. Learn. Res..

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

Trevor J. Hastie, Jason D. Lee, Reza Bosagh Zadeh, 2014, J. Mach. Learn. Res..

Missing value estimation methods for DNA microarrays

Russ B. Altman, Robert Tibshirani, David Botstein, 2001, Bioinform..

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

Trevor J. Hastie, Rahul Mazumder, T. Hastie, 2011, J. Mach. Learn. Res..

Independent Components Analysis through Product Density Estimation

Robert Tibshirani, Trevor J. Hastie, R. Tibshirani, 2002, NIPS.

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

Joel Dudley, Rong Chen, Atul J. Butte, 2010, PLoS Comput. Biol..

Principal curve approaches for inferring 3D chromatin architecture

Trevor J. Hastie, Mark R. Segal, Elena Tuzhilina, 2020, bioRxiv.

1-norm Support Vector Machines

Robert Tibshirani, Ji Zhu, Trevor J. Hastie, 2003, NIPS.

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

Trevor J. Hastie, Matthew F. Glasser, Leanne M. Williams, 2021, NeuroImage.

Model Inference and Averaging

Trevor Hastie, Robert Tibshirani, Trevor J. Hastie, 2001 .