Trevor Hastie

	2012
44		Rahul Mazumder, Trevor Hastie: Exact Covariance Thresholding into Connected Components for Large-Scale Graphical Lasso. Journal of Machine Learning Research 13: 781-794 (2012)
	2011
43		Rahul Mazumder, Trevor Hastie: The Graphical Lasso: New Insights and Alternatives CoRR abs/1111.5479: (2011)
	2010
42		Michael Greenacre, Trevor Hastie: Dynamic visualization of statistical learning in the context of high-dimensional textual data. J. Web Sem. 8(2-3): 163-168 (2010)
41		Rahul Mazumder, Trevor Hastie, Robert Tibshirani: Spectral Regularization Algorithms for Learning Large Incomplete Matrices. Journal of Machine Learning Research 11: 2287-2322 (2010)
40		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 Computational Biology 6(2): (2010)
	2009
39		Tong Tong Wu, Yi Fang Chen, Trevor Hastie, Eric M. Sobel, Kenneth Lange: Genome-wide association analysis by lasso penalized logistic regression. Bioinformatics 25(6): 714-721 (2009)
	2008
38		Trevor Hastie, Jerome Friedman, Robert Tibshirani: Regularization paths and coordinate descent. KDD 2008: 3
37		Ping Li, Kenneth Ward Church, Trevor Hastie: One sketch for all: Theory and Application of Conditional Random Sampling. NIPS 2008: 953-960
	2007
36		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
35		Ping Li, Trevor Hastie: A Unified Near-Optimal Estimator For Dimension Reduction in lalpha(0 < alpha <= 2) Using Stable Random Projections. NIPS 2007
34		Ping Li, Trevor Hastie, Kenneth Ward Church: Nonlinear Estimators and Tail Bounds for Dimension Reduction in l1 Using Cauchy Random Projections. Journal of Machine Learning Research 8: 2497-2532 (2007)
33		Robert Tibshirani, Trevor Hastie: Margin Trees for High-dimensional Classification. Journal of Machine Learning Research 8: 637-652 (2007)
	2006
32		Ping Li, Trevor Hastie, Kenneth Ward Church: Improving Random Projections Using Marginal Information. COLT 2006: 635-649
31		Ping Li, Trevor Hastie, Kenneth Ward Church: Very sparse random projections. KDD 2006: 287-296
30		Ping Li, Kenneth Ward Church, Trevor Hastie: Conditional Random Sampling: A Sketch-based Sampling Technique for Sparse Data. NIPS 2006: 873-880
29		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
28		Dirk Ormoneit, Michael J. Black, Trevor Hastie, Hedvig Kjellström: Representing cyclic human motion using functional analysis. Image Vision Comput. 23(14): 1264-1276 (2005)
	2004
27		Philip Beineke, Trevor Hastie, Shivakumar Vaithyanathan: The Sentimental Factor: Improving Review Classification Via Human-Provided Information. ACL 2004: 263-270
26		Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie: A Method for Inferring Label Sampling Mechanisms in Semi-Supervised Learning. NIPS 2004
25		Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu: The Entire Regularization Path for the Support Vector Machine. NIPS 2004
24		Robert Tibshirani, Trevor Hastie, Balasubramanian Narasimhan, Scott Soltys, Gongyi Shi, Albert Koong, Quynh-Thu Le: Sample classification from protein mass spectrometry, by 'peak probability contrasts'. Bioinformatics 20(17): 3034-3044 (2004)
23		Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu: The Entire Regularization Path for the Support Vector Machine. Journal of Machine Learning Research 5: 1391-1415 (2004)
22		Saharon Rosset, Ji Zhu, Trevor Hastie: Boosting as a Regularized Path to a Maximum Margin Classifier. Journal of Machine Learning Research 5: 941-973 (2004)
	2003
21		Saharon Rosset, Ji Zhu, Trevor Hastie: Boosting and support vector machines as optimal separators. DRR 2003: 1-7
20		Ji Zhu, Saharon Rosset, Trevor Hastie, Robert Tibshirani: 1-norm Support Vector Machines. NIPS 2003
19		Saharon Rosset, Ji Zhu, Trevor Hastie: Margin Maximizing Loss Functions. NIPS 2003
18		Trevor Hastie, Robert Tibshirani, Jerome Friedman: Note on "Comparison of Model Selection for Regression" by Vladimir Cherkassky and Yunqian Ma. Neural Computation 15(7): 1477-1480 (2003)
	2002
17		Ji Zhu, Trevor Hastie: Support Vector Machines, Kernel Logistic Regression and Boosting. Multiple Classifier Systems 2002: 16-26
16		Trevor Hastie, Robert Tibshirani: Independent Components Analysis through Product Density Estimation. NIPS 2002: 649-656
	2001
15		Ji Zhu, Trevor Hastie: Kernel Logistic Regression and the Import Vector Machine. NIPS 2001: 1081-1088
14		Olga G. Troyanskaya, Michael Cantor, Gavin Sherlock, Patrick O. Brown, Trevor Hastie, Robert Tibshirani, David Botstein, Russ B. Altman: Missing value estimation methods for DNA microarrays. Bioinformatics 17(6): 520-525 (2001)
	2000
13		Dirk Ormoneit, Hedvig Sidenbladh, Michael J. Black, Trevor Hastie: Learning and Tracking Cyclic Human Motion. NIPS 2000: 894-900
	1999
12		Dirk Ormoneit, Trevor Hastie: Optimal Kernel Shapes for Local Linear Regression. NIPS 1999: 540-546
	1998
11		Thomas D. Wu, Trevor Hastie, Scott C. Schmidler, Douglas L. Brutlag: Regression analysis of multiple protein structures. RECOMB 1998: 276-284
10		Thomas D. Wu, Scott C. Schmidler, Trevor Hastie, Douglas L. Brutlag: Regression Analysis of Multiple Protein Structures. Journal of Computational Biology 5(3): 585-596 (1998)
	1997
9		Y. Dan Rubinstein, Trevor Hastie: Discriminative vs Informative Learning. KDD 1997: 49-53
8		Trevor Hastie, Robert Tibshirani: Classification by Pairwise Coupling. NIPS 1997
7		Gareth James, Trevor Hastie: The Error Coding and Substitution PaCTs. NIPS 1997
	1996
6		Trevor Hastie, Robert Tibshirani: Discriminant Adaptive Nearest Neighbor Classification. IEEE Trans. Pattern Anal. Mach. Intell. 18(6): 607-616 (1996)
	1995
5		Trevor Hastie, Robert Tibshirani: Discriminant Adaptive Nearest Neighbor Classification. KDD 1995: 142-149
4		Trevor Hastie, Robert Tibshirani: Discriminant Adaptive Nearest Neighbor Classification and Regression. NIPS 1995: 409-415
	1994
3		Trevor Hastie, Patrice Simard: Learning Prototype Models for Tangent Distance. NIPS 1994: 999-1006
2		Winston Nelson, William Turin, Trevor Hastie: Statistical Methods for On-Line Signature Verification. IJPRAI 8(3): 749-770 (1994)
	1990
1		Eyal Kishon, Trevor Hastie, Haim J. Wolfson: 3-D Curve Matching Using Splines. ECCV 1990: 589-591

Coauthor Index

1	Russ B. Altman	[14]
2	Philip Beineke	[27]
3	Michael J. Black	[13] [28]
4	David Botstein	[14]
5	Patrick O. Brown	[14]
6	Douglas L. Brutlag	[10] [11]
7	Atul J. Butte	[40]
8	Michael Cantor	[14]
9	Rong Chen	[40]
10	Yi Fang Chen	[39]
11	Annie P. Chiang	[40]
12	Kenneth Ward Church	[29] [30] [31] [32] [34] [36] [37]
13	Joel Dudley	[40]
14	Jerome Friedman	[18] [38]
15	Michael Greenacre	[42]
16	Gareth James	[7]
17	Eyal Kishon	[1]
18	Hedvig Kjellström (Hedvig Sidenbladh)	[13] [28]
19	Albert Koong	[24]
20	Kenneth Lange	[39]
21	Quynh-Thu Le	[24]
22	Ping Li	[29] [30] [31] [32] [34] [35] [36] [37]
23	Rahul Mazumder	[41] [43] [44]
24	Balasubramanian Narasimhan	[24]
25	Winston Nelson	[2]
26	Dirk Ormoneit	[12] [13] [28]
27	Saharon Rosset	[19] [20] [21] [22] [23] [25] [26]
28	Y. Dan Rubinstein	[9]
29	Scott C. Schmidler	[10] [11]
30	Gavin Sherlock	[14]
31	Gongyi Shi	[24]
32	Patrice Y. Simard (Patrice Simard)	[3]
33	Eric M. Sobel	[39]
34	Scott Soltys	[24]
35	Silpa Suthram	[40]
36	Robert Tibshirani	[4] [5] [6] [8] [14] [16] [18] [20] [23] [24] [25] [33] [38] [41]
37	Olga G. Troyanskaya	[14]
38	William Turin	[2]
39	Shivakumar Vaithyanathan	[27]
40	Haim J. Wolfson	[1]
41	Thomas D. Wu	[10] [11]
42	Tong Tong Wu	[39]
43	Ji Zhu	[15] [17] [19] [20] [21] [22] [23] [25] [26]
44	Hui Zou	[26]

Colors in the list of coauthors

Last update Thu May 31 18:55:10 2012 CET by the DBLP Team — Data released under the ODC-BY 1.0 license — See also our legal information page