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Journal of Machine Learning Research: JMLR

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https://www.readbyqxmd.com/read/28559747/structure-leveraged-methods-in-breast-cancer-risk-prediction
#1
Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M Ong, Peggy Peissig, Elizabeth Burnside
Predicting breast cancer risk has long been a goal of medical research in the pursuit of precision medicine. The goal of this study is to develop novel penalized methods to improve breast cancer risk prediction by leveraging structure information in electronic health records. We conducted a retrospective case-control study, garnering 49 mammography descriptors and 77 high-frequency/low-penetrance single-nucleotide polymorphisms (SNPs) from an existing personalized medicine data repository. Structured mammography reports and breast imaging features have long been part of a standard electronic health record (EHR), and genetic markers likely will be in the near future...
December 2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/27635120/convex-regression-with-interpretable-sharp-partitions
#2
Ashley Petersen, Noah Simon, Daniela Witten
We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set...
June 2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28503101/l1-regularized-least-squares-for-support-recovery-of-high-dimensional-single-index-models-with-gaussian-designs
#3
Matey Neykov, Jun S Liu, Tianxi Cai
It is known that for a certain class of single index models (SIMs) [Formula: see text], support recovery is impossible when X ~
May 2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28428735/multiplicative-multitask-feature-learning
#4
Xin Wang, Jinbo Bi, Shipeng Yu, Jiangwen Sun, Minghu Song
We investigate a general framework of multiplicative multitask feature learning which decomposes individual task's model parameters into a multiplication of two components. One of the components is used across all tasks and the other component is task-specific. Several previous methods can be proved to be special cases of our framework. We study the theoretical properties of this framework when different regularization conditions are applied to the two decomposed components. We prove that this framework is mathematically equivalent to the widely used multitask feature learning methods that are based on a joint regularization of all model parameters, but with a more general form of regularizers...
April 2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28331463/a-gibbs-sampler-for-learning-dags
#5
Robert J B Goudie, Sach Mukherjee
We propose a Gibbs sampler for structure learning in directed acyclic graph (DAG) models. The standard Markov chain Monte Carlo algorithms used for learning DAGs are random-walk Metropolis-Hastings samplers. These samplers are guaranteed to converge asymptotically but often mix slowly when exploring the large graph spaces that arise in structure learning. In each step, the sampler we propose draws entire sets of parents for multiple nodes from the appropriate conditional distribution. This provides an efficient way to make large moves in graph space, permitting faster mixing whilst retaining asymptotic guarantees of convergence...
April 2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/27375369/cvxpy-a-python-embedded-modeling-language-for-convex-optimization
#6
Steven Diamond, Stephen Boyd
CVXPY is a domain-specific language for convex optimization embedded in Python. It allows the user to express convex optimization problems in a natural syntax that follows the math, rather than in the restrictive standard form required by solvers. CVXPY makes it easy to combine convex optimization with high-level features of Python such as parallelism and object-oriented design. CVXPY is available at http://www.cvxpy.org/ under the GPL license, along with documentation and examples.
April 2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/27134575/on-quantile-regression-in-reproducing-kernel-hilbert-spaces-with-data-sparsity-constraint
#7
Chong Zhang, Yufeng Liu, Yichao Wu
For spline regressions, it is well known that the choice of knots is crucial for the performance of the estimator. As a general learning framework covering the smoothing splines, learning in a Reproducing Kernel Hilbert Space (RKHS) has a similar issue. However, the selection of training data points for kernel functions in the RKHS representation has not been carefully studied in the literature. In this paper we study quantile regression as an example of learning in a RKHS. In this case, the regular squared norm penalty does not perform training data selection...
April 2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28936128/guarding-against-spurious-discoveries-in-high-dimensions
#8
Jianqing Fan, Wen-Xin Zhou
Many data-mining and statistical machine learning algorithms have been developed to select a subset of covariates to associate with a response variable. Spurious discoveries can easily arise in high-dimensional data analysis due to enormous possibilities of such selections. How can we know statistically our discoveries better than those by chance? In this paper, we define a measure of goodness of spurious fit, which shows how good a response variable can be fitted by an optimally selected subset of covariates under the null model, and propose a simple and effective LAMM algorithm to compute it...
2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28066157/support-vector-hazards-machine-a-counting-process-framework-for-learning-risk-scores-for-censored-outcomes
#9
Yuanjia Wang, Tianle Chen, Donglin Zeng
Learning risk scores to predict dichotomous or continuous outcomes using machine learning approaches has been studied extensively. However, how to learn risk scores for time-to-event outcomes subject to right censoring has received little attention until recently. Existing approaches rely on inverse probability weighting or rank-based regression, which may be inefficient. In this paper, we develop a new support vector hazards machine (SVHM) approach to predict censored outcomes. Our method is based on predicting the counting process associated with the time-to-event outcomes among subjects at risk via a series of support vector machines...
2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28018133/multi-objective-markov-decision-processes-for-data-driven-decision-support
#10
Daniel J Lizotte, Eric B Laber
We present new methodology based on Multi-Objective Markov Decision Processes for developing sequential decision support systems from data. Our approach uses sequential decision-making data to provide support that is useful to many different decision-makers, each with different, potentially time-varying preference. To accomplish this, we develop an extension of fitted-Q iteration for multiple objectives that computes policies for all scalarization functions, i.e. preference functions, simultaneously from continuous-state, finite-horizon data...
2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/27746703/extracting-pico-sentences-from-clinical-trial-reports-using-supervised-distant-supervision
#11
Byron C Wallace, Joël Kuiper, Aakash Sharma, Mingxi Brian Zhu, Iain J Marshall
Systematic reviews underpin Evidence Based Medicine (EBM) by addressing precise clinical questions via comprehensive synthesis of all relevant published evidence. Authors of systematic reviews typically define a Population/Problem, Intervention, Comparator, and Outcome (a PICO criteria) of interest, and then retrieve, appraise and synthesize results from all reports of clinical trials that meet these criteria. Identifying PICO elements in the full-texts of trial reports is thus a critical yet time-consuming step in the systematic review process...
2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/27239164/a-consistent-information-criterion-for-support-vector-machines-in-diverging-model-spaces
#12
Xiang Zhang, Yichao Wu, Lan Wang, Runze Li
Information criteria have been popularly used in model selection and proved to possess nice theoretical properties. For classification, Claeskens et al. (2008) proposed support vector machine information criterion for feature selection and provided encouraging numerical evidence. Yet no theoretical justification was given there. This work aims to fill the gap and to provide some theoretical justifications for support vector machine information criterion in both fixed and diverging model spaces. We first derive a uniform convergence rate for the support vector machine solution and then show that a modification of the support vector machine information criterion achieves model selection consistency even when the number of features diverges at an exponential rate of the sample size...
2016: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/27570498/graphical-models-via-univariate-exponential-family-distributions
#13
Eunho Yang, Pradeep Ravikumar, Genevera I Allen, Zhandong Liu
Undirected graphical models, or Markov networks, are a popular class of statistical models, used in a wide variety of applications. Popular instances of this class include Gaussian graphical models and Ising models. In many settings, however, it might not be clear which subclass of graphical models to use, particularly for non-Gaussian and non-categorical data. In this paper, we consider a general sub-class of graphical models where the node-wise conditional distributions arise from exponential families. This allows us to derive multivariate graphical model distributions from univariate exponential family distributions, such as the Poisson, negative binomial, and exponential distributions...
December 2015: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28316509/calibrated-multivariate-regression-with-application-to-neural-semantic-basis-discovery
#14
Han Liu, Lie Wang, Tuo Zhao
We propose a calibrated multivariate regression method named CMR for fitting high dimensional multivariate regression models. Compared with existing methods, CMR calibrates regularization for each regression task with respect to its noise level so that it simultaneously attains improved finite-sample performance and tuning insensitiveness. Theoretically, we provide sufficient conditions under which CMR achieves the optimal rate of convergence in parameter estimation. Computationally, we propose an efficient smoothed proximal gradient algorithm with a worst-case numerical rate of convergence O(1/ϵ), where ϵ is a pre-specified accuracy of the objective function value...
August 2015: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/28337074/the-flare-package-for-high-dimensional-linear-regression-and-precision-matrix-estimation-in-r
#15
Xingguo Li, Tuo Zhao, Xiaoming Yuan, Han Liu
This paper describes an R package named flare, which implements a family of new high dimensional regression methods (LAD Lasso, SQRT Lasso, ℓ q Lasso, and Dantzig selector) and their extensions to sparse precision matrix estimation (TIGER and CLIME). These methods exploit different nonsmooth loss functions to gain modeling exibility, estimation robustness, and tuning insensitiveness. The developed solver is based on the alternating direction method of multipliers (ADMM), which is further accelerated by the multistage screening approach...
March 2015: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/26568704/joint-estimation-of-multiple-precision-matrices-with-common-structures
#16
Wonyul Lee, Yufeng Liu
Estimation of inverse covariance matrices, known as precision matrices, is important in various areas of statistical analysis. In this article, we consider estimation of multiple precision matrices sharing some common structures. In this setting, estimating each precision matrix separately can be suboptimal as it ignores potential common structures. This article proposes a new approach to parameterize each precision matrix as a sum of common and unique components and estimate multiple precision matrices in a constrained l 1 minimization framework...
2015: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/25620891/learning-graphical-models-with-hubs
#17
Kean Ming Tan, Palma London, Karthik Mohan, Su-In Lee, Maryam Fazel, Daniela Witten
We consider the problem of learning a high-dimensional graphical model in which there are a few hub nodes that are densely-connected to many other nodes. Many authors have studied the use of an ℓ 1 penalty in order to learn a sparse graph in the high-dimensional setting. However, the ℓ 1 penalty implicitly assumes that each edge is equally likely and independent of all other edges. We propose a general framework to accommodate more realistic networks with hub nodes, using a convex formulation that involves a row-column overlap norm penalty...
October 2014: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/25620892/graph-estimation-from-multi-attribute-data
#18
Mladen Kolar, Han Liu, Eric P Xing
Undirected graphical models are important in a number of modern applications that involve exploring or exploiting dependency structures underlying the data. For example, they are often used to explore complex systems where connections between entities are not well understood, such as in functional brain networks or genetic networks. Existing methods for estimating structure of undirected graphical models focus on scenarios where each node represents a scalar random variable, such as a binary neural activation state or a continuous mRNA abundance measurement, even though in many real world problems, nodes can represent multivariate variables with much richer meanings, such as whole images, text documents, or multi-view feature vectors...
May 2014: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/25620890/the-fastclime-package-for-linear-programming-and-large-scale-precision-matrix-estimation-in-r
#19
Haotian Pang, Han Liu, Robert Vanderbei
We develop an R package fastclime for solving a family of regularized linear programming (LP) problems. Our package efficiently implements the parametric simplex algorithm, which provides a scalable and sophisticated tool for solving large-scale linear programs. As an illustrative example, one use of our LP solver is to implement an important sparse precision matrix estimation method called CLIME (Constrained L 1 Minimization Estimator). Compared with existing packages for this problem such as clime and flare, our package has three advantages: (1) it efficiently calculates the full piecewise-linear regularization path; (2) it provides an accurate dual certificate as stopping criterion; (3) it is completely coded in C and is highly portable...
February 2014: Journal of Machine Learning Research: JMLR
https://www.readbyqxmd.com/read/25580094/confidence-intervals-for-random-forests-the-jackknife-and-the-infinitesimal-jackknife
#20
Stefan Wager, Trevor Hastie, Bradley Efron
We study the variability of predictions made by bagged learners and random forests, and show how to estimate standard errors for these methods. Our work builds on variance estimates for bagging proposed by Efron (1992, 2013) that are based on the jackknife and the infinitesimal jackknife (IJ). In practice, bagged predictors are computed using a finite number B of bootstrap replicates, and working with a large B can be computationally expensive. Direct applications of jackknife and IJ estimators to bagging require B = Θ(n (1...
January 2014: Journal of Machine Learning Research: JMLR
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