Sparse estimation of large covariance matrices via a nested Lasso penalty (2008)
Levina, Elizaveta, Rothman, Adam, Zhu, Ji
The paper proposes a new covariance estimator for large covariance matrices when the variables have a natural ordering. Using the Cholesky decomposition of the inverse, we impose a banded structure...
Variable selection for the multicategory SVM via adaptive sup-norm regularization (2008)
Zhang, Hao Helen, Liu, Yufeng, Wu, Yichao, Zhu, Ji
The Support Vector Machine (SVM) is a popular classification paradigm in machine learning and has achieved great success in real applications. However, the standard SVM can not select variables...
Sparse permutation invariant covariance estimation (2008)
Rothman, Adam J., Bickel, Peter J., Levina, Elizaveta, Zhu, Ji
The paper proposes a method for constructing a sparse estimator for the inverse covariance (concentration) matrix in high-dimensional settings. The estimator uses a penalized normal likelihood...
Piecewise linear regularized solution paths (2007)
We consider the generic regularized optimization problem $\hat{\mathsf{\beta}}(\lambda)=\arg \min_{\beta}L({\sf{y}},X{\sf{\beta}})+\lambda J({\sf{\beta}})$. Efron, Hastie, Johnstone and Tibshirani...
Comment on "Support Vector Machines with Applications" (2006)
Comment on [math.ST/0612817]
Doubly Penalized Buckley-James Method for Survival Data with High-Dimensional Covariates (2006)
Wang, Sijian, Nan, Bin, Zhu, Ji, Beer, David G.
Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley-James method...
Doubly Penalized Buckley-James Method for Survival Data with High-Dimensional Covariates (2006)
Wang, Sijian, Nan, Bin, Zhu, Ji, Beer, David G.
Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley-James method...
Doubly Penalized Buckley-James Method for Survival Data with High-Dimensional Covariates (2006)
Wang, Sijian, Nan, Bin, Zhu, Ji, Beer, David G.
Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley-James method...
Doubly Penalized Buckley-James Method for Survival Data with High-Dimensional Covariates (2006)
Wang, Sijian, Nan, Bin, Zhu, Ji, Beer, David G.
Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley-James method...
Studies of Stability and Robustness for Artificial Neural Networks and Boosted Decision Trees (2006)
Yang, Hai-Jun, Roe, Byron P., Zhu, Ji
In this paper, we compare the performance, stability and robustness of Artificial Neural Networks (ANN) and Boosted Decision Trees (BDT) using MiniBooNE Monte Carlo samples. These methods attempt to...
Studies of Boosted Decision Trees for MiniBooNE Particle Identification (2005)
Yang, Hai-Jun, Roe, Byron P., Zhu, Ji
Boosted decision trees are applied to particle identification in the MiniBooNE experiment operated at Fermi National Accelerator Laboratory (Fermilab) for neutrino oscillations. Numerous attempts are...
Roe, Byron P., Yang, Hai-Jun, Zhu, Ji, Liu, Yong, Stancu, Ion, McGregor, Gordon
The efficacy of particle identification is compared using artificial neutral networks and boosted decision trees. The comparison is performed in the context of the MiniBooNE, an experiment at...
Saharon Rosset, Ji Zhu, Trevor Hastie
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...
Discussion of "Least angle regression" by Efron et al (2004)
Discussion of ``Least angle regression'' by Efron et al. [math.ST/0406456]
Automatic Bayes Carpentry Using Unlabeled Data In Semi-Supervised Classification (2004)
Hui Zou, Ji Zhu, Trevor Hastie
In semi-supervised classification one important question is how to use the enormous amount of unlabeled data to improve the classifier built on the labeled data. We point out that under certain...
A Method for Inferring Label Sampling Mechanisms In Semi-Supervised Learning (2004)
Saharon Rosset, Ji Zhu, Hui Zou, Trevor Hastie
We consider the situation in semi-supervised learning, where the "label sampling" mechanism stochastically depends on the true response (as well as potentially on the features). We suggest a method...
The Entire Regularization Path for the Support (2004)
Trevor Hastie, Saharon Rosset, Robert Tibshirani, Ji Zhu
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...
The Entire Regularization Path for the Support Vector (2004)
Trevor Hastie, Saharon Rosset, Rob Tibshirani, Ji Zhu
The Support Vector Machine is a widely used tool for classification. Many e#cient implementations exist for fitting a two-class SVM model. The user has to supply values for the tuning parameters: the...
Margin Maximizing Loss Functions (2004)
Saharon Rosset, Ji Zhu, Trevor Hastie
Margin maximizing properties play an important role in the analysis of classi- cation models, such as boosting and support vector machines. Margin maximization is theoretically interesting because it...
norm Support Vector Machines (2004)
Ji Zhu, Saharon Rosset, Trevor Hastie, Rob Tibshirani
The standard 2-norm SVM is known for its good performance in twoclass classication. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard...
Discussions of boosting papers, and rejoinders (2004)
Bartlett, Peter L., Bickel, Peter J., Bühlmann, Peter, Freund, Yoav, Friedman, Jerome, Hastie, Trevor, ...
Discussions of: "Process consistency for AdaBoost" [Ann. Statist. 32 (2004), no. 1, 13-29] by W. Jiang; "On the Bayes-risk consistency of regularized boosting methods" [ibid., 30-55] by G. Lugosi and...
Kernel Logistic Regression and the Import Vector Machine Ji Zhu (2004)
The support vector machine (SVM) is known for its good performance in binary classification, but its extension to multi-class classification is still an on-going research issue. In this paper, we...
Fabrication, characterization and reaction studies of nanofabricated platinum model catalysts (2003)
1-norm Support Vector Machines (2003)
Ji Zhu, Saharon Rosset, Trevor Hastie, Rob Tibshirani
The standard 2-norm SVM is known for its good performance in twoclass classification. In this paper, we consider the 1-norm SVM. We argue that the 1-norm SVM may have some advantage over the standard...
Margin Maximizing Loss Functions (2003)
Saharon Rosset, Ji Zhu, Trevor Hastie
Margin maximizing properties play an important role in the analysis of classification models, such as boosting and support vector machines. Margin maximization is theoretically interesting because it...
Boosting as a Regularized Path to a Maximum (2003)
Saharon Rosset, Ji Zhu, Trevor Hastie
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...
Thesis (Ph. D. in Chemistry)--University of California, Berkeley, Fall 2003.
Kernel Logistic Regression and the Import Vector Machine (2002)
The support vector machine (SVM) is known for its good performance in binary classification, but its extension to multi-class classification is still an on-going research issue. In this paper, we...
Kernel Logistic Regression and the Import Vector Machine (2001)
The support vector machine (SVM) is known for its good performance in binary classification, but its extension to multi-class classification is still an on-going research issue. In this paper, we...
Degree granted in Chemistry.
Sparsity and smoothness via the fused lasso
Robert Tibshirani, Michael Saunders, Saharon Rosset, Ji Zhu, Keith Knight
The lasso penalizes a least squares regression by the sum of the absolute values ("L"1-norm) of the coefficients. The form of this penalty encourages sparse solutions (with many coefficients equal to...
An Interrelationship Between Autophagy and Filamentous Growth in Budding Yeast
Ma, Jun, Jin, Rui, Jia, Xiaoyu, Dobry, Craig J., Wang, Li, Reggiori, Fulvio, ...
Over the last 15 years, yeast pseudohyphal growth (PHG) has been the focus of intense research interest as a model of fungal pathogenicity. Specifically, PHG is a stress response wherein yeast cells...