Department of Statistics
Stanford University
Stanford, CA 94305
Email: jhf@stanford.edu
Fax: +1 650 725 8977
Courses
Simon, N., Friedman J. H., and Hastie, T. A blockwise descent algorithm for group-penalized multiresponse and multinomial regression.(2012)
Witten D. M., Friedman J. H., and Simon N. New insights and faster computations for the graphical lasso. (2011)
Simon, N., Friedman, J. H., Hastie T. and Tibshirani R. Regularization Paths for Cox's Proportional Hazards Model via Coordinate Descent. (2011)
Tibshirani, R., Bien, J., Friedman, J. H., Hastie, T., Simon, N., Taylor, J., and Tibshirani, R. Strong rules for discarding predictors in lasso-type problems. (2010)
Friedman, J. H., Hastie, T. and Tibshirani, R. Applications of the lasso and grouped lasso to the estimation of sparse graphical models. (2010)
Friedman, J. H., Hastie, T. and Tibshirani, R. A note on the group lasso and a sparse group lasso. (2010)
Mazumder, R., Friedman, J. H., and Hastie, T. SparseNet Coordinate Descent with Non-Convex Penalties. (2009 - in press, JASA 2011)
Hastie, T., Tibshirani, R., and Friedman, J. H. Elements of Statistical Learning: Data Mining, Inference and Prediction (Second Edition). Springer-Verlag, New York.
Friedman, J. H., Hastie, T. and Tibshirani, R. Regularized Paths for Generalized Linear Models via Coordinate Descent. Journal of Statistical Software, 33(1) (2008)
Friedman, J. H., Hastie, T. and Tibshirani, R. Sparse inverse covariance estimation with the lasso. (2008)
Friedman, J. H. "Fast sparse regression and classification." (2008) (software)
Friedman, J. H., Hastie, T. and Tibshirani, R. Discussion of "Evidence contrary to the statistical view of boosting (David Mease and Aaron Wyner)" JMLR9 (2008) 59-64.
Friedman, J. H., Hastie, T., Hoefling H., and Tibshirani, R. Pathwise Coordinate Optimization. Annals Applied Statistics 1, 302-332 (2007)
Friedman, J. H., Hastie T, Hofling., H., Tibshirani R. "Pathwise coordinate optimization." (2007).
Friedman, J. H., Hastie T, Tibshirani R. "Sparse inverse covariance estimation with the graphical lasso." (2007)
Friedman, J. H. and Popescu, B. E. "Predictive Learning via Rule Ensembles." (Feb. 2005) (software)
Friedman, J. H. and Popescu, B. E. "Gradient directed regularization." (Feb. 2004) (long version) (software)
Friedman, J. H. "On multivariate goodness-of-fit and two-sample testing." (Dec. 2003)
Friedman, J. H. "Recent advances in predictive (machine) learning." (Nov. 2003)
Friedman, J. H. and Popescu, B. E. "Importance Sampled Learning Ensembles." (Sept. 2003)
Friedman, J. H. and Meulman, J. J. "Clustering Objects on Subsets of Attributes." (June 2002) (software)
Friedman, J. H. "Tutorial: Getting Started with MART in R." (April 2002) (software)
Friedman, J. H. and Hall , P. "On Bagging and Nonlinear Estimation." (May 1999)
Friedman, J. H. "Stochastic Gradient Boosting ." (March 1999b) (software)
Friedman, J. H. "Greedy Function Approximation: A Gradient Boosting Machine." (Feb. 1999a) (software)
Friedman, J. H. and Fisher, N. I. "Rejoinder to discussion of: Bump Hunting in High-Dimensional Data." (Nov. 1998) (software)
Friedman, J. H., Hastie, T. and Tibshirani, R. "Additive Logistic Regression: a Statistical View of Boosting." (Aug. 1998)
Friedman, J. H. "Data Mining and Statistics: What's the Connection?" (Nov. 1997b).
Friedman, J. H. and Fisher, N. I. "Bump Hunting in High-Dimensional Data." (Oct. 1997a). (software)
Friedman, J. H "DART/HYESS Users Guide " (Dec. 1996b). (software)
Friedman, J. H "Another Approach to Polychotomous Classification" (Oct. 1996).
Friedman, J. H "Local Learning Based on Recursive Covering" (Aug. 1996a). (software)
Friedman, J. H "On Bias, Variance, 0/1-loss, and the Curse-of-Dimensionality " (April. 1996).
Friedman, J. H. "Flexible Metric Nearest Neighbor Classification." (Nov. 1994).