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Journal of machine learning research : JMLR最新文献

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Causal Learning via Manifold Regularization 基于流形正则化的因果学习
Journal of machine learning research : JMLR Pub Date : 2016-12-16 DOI: 10.17863/CAM.44718
Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, S. Mukherjee
{"title":"Causal Learning via Manifold Regularization","authors":"Steven M. Hill, Chris J. Oates, Duncan A. J. Blythe, S. Mukherjee","doi":"10.17863/CAM.44718","DOIUrl":"https://doi.org/10.17863/CAM.44718","url":null,"abstract":"This paper frames causal structure estimation as a machine learning task. The idea is to treat indicators of causal relationships between variables as ‘labels’ and to exploit available data on the variables of interest to provide features for the labelling task. Background scientific knowledge or any available interventional data provide labels on some causal relationships and the remainder are treated as unlabelled. To illustrate the key ideas, we develop a distance-based approach (based on bivariate histograms) within a manifold regularization framework. We present empirical results on three different biological data sets (including examples where causal effects can be verified by experimental intervention), that together demonstrate the efficacy and general nature of the approach as well as its simplicity from a user’s point of view.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"25 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125964285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 7
Matrix completion and low-rank SVD via fast alternating least squares 基于快速交替最小二乘的矩阵补全和低秩奇异值分解
Journal of machine learning research : JMLR Pub Date : 2014-10-09 DOI: 10.5555/2789272.2912106
T. Hastie, R. Mazumder, J. Lee, R. Zadeh
{"title":"Matrix completion and low-rank SVD via fast alternating least squares","authors":"T. Hastie, R. Mazumder, J. Lee, R. Zadeh","doi":"10.5555/2789272.2912106","DOIUrl":"https://doi.org/10.5555/2789272.2912106","url":null,"abstract":"The matrix-completion problem has attracted a lot of attention, largely as a result of the celebrated Netflix competition. Two popular approaches for solving the problem are nuclear-norm-regularized matrix approximation (Candès and Tao, 2009; Mazumder et al., 2010), and maximum-margin matrix factorization (Srebro et al., 2005). These two procedures are in some cases solving equivalent problems, but with quite different algorithms. In this article we bring the two approaches together, leading to an efficient algorithm for large matrix factorization and completion that outperforms both of these. We develop a software package softlmpute in R for implementing our approaches, and a distributed version for very large matrices using the Spark cluster programming environment.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"64 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126299112","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 433
Learning graphical models with hubs 使用集线器学习图形模型
Journal of machine learning research : JMLR Pub Date : 2014-02-28 DOI: 10.5555/2627435.2697070
Kean Ming Tan, Palma London, Karthika Mohan, Su-In Lee, Maryam Fazel, D. Witten
{"title":"Learning graphical models with hubs","authors":"Kean Ming Tan, Palma London, Karthika Mohan, Su-In Lee, Maryam Fazel, D. Witten","doi":"10.5555/2627435.2697070","DOIUrl":"https://doi.org/10.5555/2627435.2697070","url":null,"abstract":"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 ℓ1 penalty in order to learn a sparse graph in the high-dimensional setting. However, the ℓ1 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. We apply this general framework to three widely-used probabilistic graphical models: the Gaussian graphical model, the covariance graph model, and the binary Ising model. An alternating direction method of multipliers algorithm is used to solve the corresponding convex optimization problems. On synthetic data, we demonstrate that our proposed framework outperforms competitors that do not explicitly model hub nodes. We illustrate our proposal on a webpage data set and a gene expression data set.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2014-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132180895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 89
Confidence intervals for random forests: the jackknife and the infinitesimal jackknife 随机森林的置信区间:折刀和无穷小折刀
Journal of machine learning research : JMLR Pub Date : 2013-11-18 DOI: 10.5555/2627435.2638587
Stefan Wager, T. Hastie, B. Efron
{"title":"Confidence intervals for random forests: the jackknife and the infinitesimal jackknife","authors":"Stefan Wager, T. Hastie, B. Efron","doi":"10.5555/2627435.2638587","DOIUrl":"https://doi.org/10.5555/2627435.2638587","url":null,"abstract":"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(n1.5) bootstrap replicates to converge, where n is the size of the training set. We propose improved versions that only require B = Θ(n) replicates. Moreover, we show that the IJ estimator requires 1.7 times less bootstrap replicates than the jackknife to achieve a given accuracy. Finally, we study the sampling distributions of the jackknife and IJ variance estimates themselves. We illustrate our findings with multiple experiments and simulation studies.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-11-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130574198","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 358
Distributions of angles in random packing on spheres 球面上随机填料的角度分布
Journal of machine learning research : JMLR Pub Date : 2013-06-02 DOI: 10.5555/2567709.2567722
Tony Cai, Jianqing Fan, Tiefeng Jiang
{"title":"Distributions of angles in random packing on spheres","authors":"Tony Cai, Jianqing Fan, Tiefeng Jiang","doi":"10.5555/2567709.2567722","DOIUrl":"https://doi.org/10.5555/2567709.2567722","url":null,"abstract":"This paper studies the asymptotic behaviors of the pairwise angles among n randomly and uniformly distributed unit vectors in [Formula: see text] as the number of points n → ∞, while the dimension p is either fixed or growing with n. For both settings, we derive the limiting empirical distribution of the random angles and the limiting distributions of the extreme angles. The results reveal interesting differences in the two settings and provide a precise characterization of the folklore that \"all high-dimensional random vectors are almost always nearly orthogonal to each other\". Applications to statistics and machine learning and connections with some open problems in physics and mathematics are also discussed.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"14 1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130271967","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 164
Linear fitted-Q iteration with multiple reward functions 具有多个奖励函数的线性拟合q迭代
Journal of machine learning research : JMLR Pub Date : 2013-06-02 DOI: 10.5555/2503308.2503346
D. Lizotte, Michael Bowling, S. Murphy
{"title":"Linear fitted-Q iteration with multiple reward functions","authors":"D. Lizotte, Michael Bowling, S. Murphy","doi":"10.5555/2503308.2503346","DOIUrl":"https://doi.org/10.5555/2503308.2503346","url":null,"abstract":"We present a general and detailed development of an algorithm for finite-horizon fitted-Q iteration with an arbitrary number of reward signals and linear value function approximation using an arbitrary number of state features. This includes a detailed treatment of the 3-reward function case using triangulation primitives from computational geometry and a method for identifying globally dominated actions. We also present an example of how our methods can be used to construct a real-world decision aid by considering symptom reduction, weight gain, and quality of life in sequential treatments for schizophrenia. Finally, we discuss future directions in which to take this work that will further enable our methods to make a positive impact on the field of evidence-based clinical decision support.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"55 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-06-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120975126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 93
Calibrated multivariate regression with application to neural semantic basis discovery 校正多元回归及其在神经语义基发现中的应用
Journal of machine learning research : JMLR Pub Date : 2013-05-10 DOI: 10.5555/2789272.2886800
Han Liu, Lie Wang, Tuo Zhao
{"title":"Calibrated multivariate regression with application to neural semantic basis discovery","authors":"Han Liu, Lie Wang, Tuo Zhao","doi":"10.5555/2789272.2886800","DOIUrl":"https://doi.org/10.5555/2789272.2886800","url":null,"abstract":"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. We conduct thorough numerical simulations to illustrate that CMR consistently outperforms other high dimensional multivariate regression methods. We also apply CMR to solve a brain activity prediction problem and find that it is as competitive as a handcrafted model created by human experts. The R package camel implementing the proposed method is available on the Comprehensive R Archive Network http://cran.r-project.org/web/packages/camel/.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"28 5","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133043306","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 34
Node-based learning of multiple Gaussian graphical models 基于节点的多高斯图形模型学习
Journal of machine learning research : JMLR Pub Date : 2013-03-20 DOI: 10.5555/2627435.2627448
Karthika Mohan, Palma London, Maryam Fazel, D. Witten, Su-In Lee
{"title":"Node-based learning of multiple Gaussian graphical models","authors":"Karthika Mohan, Palma London, Maryam Fazel, D. Witten, Su-In Lee","doi":"10.5555/2627435.2627448","DOIUrl":"https://doi.org/10.5555/2627435.2627448","url":null,"abstract":"We consider the problem of estimating high-dimensional Gaussian graphical models corresponding to a single set of variables under several distinct conditions. This problem is motivated by the task of recovering transcriptional regulatory networks on the basis of gene expression data containing heterogeneous samples, such as different disease states, multiple species, or different developmental stages. We assume that most aspects of the conditional dependence networks are shared, but that there are some structured differences between them. Rather than assuming that similarities and differences between networks are driven by individual edges, we take a node-based approach, which in many cases provides a more intuitive interpretation of the network differences. We consider estimation under two distinct assumptions: (1) differences between the K networks are due to individual nodes that are perturbed across conditions, or (2) similarities among the K networks are due to the presence of common hub nodes that are shared across all K networks. Using a row-column overlap norm penalty function, we formulate two convex optimization problems that correspond to these two assumptions. We solve these problems using an alternating direction method of multipliers algorithm, and we derive a set of necessary and sufficient conditions that allows us to decompose the problem into independent subproblems so that our algorithm can be scaled to high-dimensional settings. Our proposal is illustrated on synthetic data, a webpage data set, and a brain cancer gene expression data set.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"58 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126010385","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 187
Graphical models via univariate exponential family distributions 通过单变量指数族分布的图形模型
Journal of machine learning research : JMLR Pub Date : 2013-01-17 DOI: 10.5555/2789272.2912117
Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu
{"title":"Graphical models via univariate exponential family distributions","authors":"Eunho Yang, Pradeep Ravikumar, Genevera I. Allen, Zhandong Liu","doi":"10.5555/2789272.2912117","DOIUrl":"https://doi.org/10.5555/2789272.2912117","url":null,"abstract":"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. Our key contributions include a class of M-estimators to fit these graphical model distributions; and rigorous statistical analysis showing that these M-estimators recover the true graphical model structure exactly, with high probability. We provide examples of genomic and proteomic networks learned via instances of our class of graphical models derived from Poisson and exponential distributions.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"63 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126024565","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 161
Pairwise likelihood ratios for estimation of non-Gaussian structural equation models 非高斯结构方程模型估计的成对似然比
Journal of machine learning research : JMLR Pub Date : 2013-01-01 DOI: 10.5555/2567709.2502585
Aapo Hyvärinen, Stephen M. Smith
{"title":"Pairwise likelihood ratios for estimation of non-Gaussian structural equation models","authors":"Aapo Hyvärinen, Stephen M. Smith","doi":"10.5555/2567709.2502585","DOIUrl":"https://doi.org/10.5555/2567709.2502585","url":null,"abstract":"We present new measures of the causal direction, or direction of effect, between two non-Gaussian random variables. They are based on the likelihood ratio under the linear non-Gaussian acyclic model (LiNGAM). We also develop simple first-order approximations of the likelihood ratio and analyze them based on related cumulant-based measures, which can be shown to find the correct causal directions. We show how to apply these measures to estimate LiNGAM for more than two variables, and even in the case of more variables than observations. We further extend the method to cyclic and nonlinear models. The proposed framework is statistically at least as good as existing ones in the cases of few data points or noisy data, and it is computationally and conceptually very simple. Results on simulated fMRI data indicate that the method may be useful in neuroimaging where the number of time points is typically quite small.","PeriodicalId":314696,"journal":{"name":"Journal of machine learning research : JMLR","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2013-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122105801","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 183
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