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The lasso penalizes a least squares regression by the sum of the absolute values (L1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many coefficients equal to 0).We propose the ‘fused lasso’, a generalization that is designed for problems with features that can be ordered in some meaningful way. The fused lasso… (More)

- Trevor J. Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu
- Journal of Machine Learning Research
- 2004

In this paper we argue that the choice of the SVM cost parameter can be critical. We then derive an algorithm that can fit the entire path of SVM solutions for every value of the cost parameter, with essentially the same computational cost as fitting one SVM model.

- Ji Zhu, Saharon Rosset, Trevor J. Hastie, Robert Tibshirani
- NIPS
- 2003

The standard 2-norm SVM is known for its good performance in twoclass classi£cation. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard 2-norm SVM, especially when there are redundant noise features. We also propose an ef£cient algorithm that computes the whole solution path of the 1-norm SVM,… (More)

- Ji Zhu, Hui Zou, Saharon Rosset, Trevor Hastie
- 2005

Boosting has been a very successful technique for solving the two-class classification problem. In going from two-class to multi-class classification, most algorithms have been restricted to reducing the multi-class classification problem to multiple two-class problems. In this paper, we develop a new algorithm that directly extends the AdaBoost algorithm… (More)

- Saharon Rosset, Ji Zhu, Trevor J. Hastie
- Journal of Machine Learning Research
- 2004

In this paper we study boosting methods from a new perspective. We build on recent work by Efron et al. to show that boosting approximately (and in some cases exactly) minimizes its loss criterion with an l1 constraint on the coefficient vector. This helps understand the success of boosting with early stopping as regularized fitting of the loss criterion.… (More)

- Doron M Behar, Richard Villems, +12 authors Saharon Rosset
- American journal of human genetics
- 2008

The quest to explain demographic history during the early part of human evolution has been limited because of the scarce paleoanthropological record from the Middle Stone Age. To shed light on the structure of the mitochondrial DNA (mtDNA) phylogeny at the dawn of Homo sapiens, we constructed a matrilineal tree composed of 624 complete mtDNA genomes from… (More)

- Doron M Behar, Mannis van Oven, +6 authors Richard Villems
- American journal of human genetics
- 2012

Mutational events along the human mtDNA phylogeny are traditionally identified relative to the revised Cambridge Reference Sequence, a contemporary European sequence published in 1981. This historical choice is a continuous source of inconsistencies, misinterpretations, and errors in medical, forensic, and population genetic studies. Here, after having… (More)

- Doron M Behar, Bayazit Yunusbayev, +18 authors Richard Villems
- Nature
- 2010

Contemporary Jews comprise an aggregate of ethno-religious communities whose worldwide members identify with each other through various shared religious, historical and cultural traditions. Historical evidence suggests common origins in the Middle East, followed by migrations leading to the establishment of communities of Jews in Europe, Africa and Asia, in… (More)

- Aurelie C. Lozano, Naoki Abe, Yan Liu, Saharon Rosset
- Bioinformatics
- 2009

We consider the problem of discovering gene regulatory networks from time-series microarray data. Recently, graphical Granger modeling has gained considerable attention as a promising direction for addressing this problem. These methods apply graphical modeling methods on time-series data and invoke the notion of 'Granger causality' to make assertions on… (More)

- Noureddine El Karoui, Alexandre d’Aspremont, +5 authors NOUREDDINE EL KAROUI
- 2008

Estimating the eigenvalues of a population covariance matrix from a sample covariance matrix is a problem of fundamental importance in multivariate statistics; the eigenvalues of covariance matrices play a key role in many widely techniques, in particular in Principal Component Analysis (PCA). In many modern data analysis problems, statisticians are faced… (More)