Journal of Machine Learning Research最新文献_第5页

A flexible model-free prediction-based framework for feature ranking. 一个灵活的、无模型的、基于预测的特征排序框架。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-05-01

Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong

{"title":"A flexible model-free prediction-based framework for feature ranking.","authors":"Jingyi Jessica Li, Yiling Elaine Chen, Xin Tong","doi":"","DOIUrl":"","url":null,"abstract":"Despite the availability of numerous statistical and machine learning tools for joint feature modeling, many scientists investigate features marginally, i.e., one feature at a time. This is partly due to training and convention but also roots in scientists' strong interests in simple visualization and interpretability. As such, marginal feature ranking for some predictive tasks, e.g., prediction of cancer driver genes, is widely practiced in the process of scientific discoveries. In this work, we focus on marginal ranking for binary classification, one of the most common predictive tasks. We argue that the most widely used marginal ranking criteria, including the Pearson correlation, the two-sample t test, and two-sample Wilcoxon rank-sum test, do not fully take feature distributions and prediction objectives into account. To address this gap in practice, we propose two ranking criteria corresponding to two prediction objectives: the classical criterion (CC) and the Neyman-Pearson criterion (NPC), both of which use model-free nonparametric implementations to accommodate diverse feature distributions. Theoretically, we show that under regularity conditions, both criteria achieve sample-level ranking that is consistent with their population-level counterpart with high probability. Moreover, NPC is robust to sampling bias when the two class proportions in a sample deviate from those in the population. This property endows NPC good potential in biomedical research where sampling biases are ubiquitous. We demonstrate the use and relative advantages of CC and NPC in simulation and real data studies. Our model-free objective-based ranking idea is extendable to ranking feature subsets and generalizable to other prediction tasks and learning objectives.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"22 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2021-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8939838/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10265462","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints. 数据共享约束下的异质性整合高维多重测试

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-04-01

Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai

{"title":"Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints.","authors":"Molei Liu, Yin Xia, Kelly Cho, Tianxi Cai","doi":"","DOIUrl":"","url":null,"abstract":"Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensional setting often fails due to the limited sample size. One approach to improving power is through meta-analyzing multiple studies which address the same scientific question. However, integrative analysis of high dimensional data from multiple studies is challenging in the presence of between-study heterogeneity. The challenge is even more pronounced with additional data sharing constraints under which only summary data can be shared across different sites. In this paper, we propose a novel data shielding integrative large-scale testing (DSILT) approach to signal detection allowing between-study heterogeneity and not requiring the sharing of individual level data. Assuming the underlying high dimensional regression models of the data differ across studies yet share similar support, the proposed method incorporates proper integrative estimation and debiasing procedures to construct test statistics for the overall effects of specific covariates. We also develop a multiple testing procedure to identify significant effects while controlling the false discovery rate (FDR) and false discovery proportion (FDP). Theoretical comparisons of the new testing procedure with the ideal individual-level meta-analysis (ILMA) approach and other distributed inference methods are investigated. Simulation studies demonstrate that the proposed testing procedure performs well in both controlling false discovery and attaining power. The new method is applied to a real example detecting interaction effects of the genetic variants for statins and obesity on the risk for type II diabetes.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"22 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2021-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10327421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9811440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace. 具有共同不变子空间的多个异构网络的推理。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-03-01

Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E Priebe, Joshua T Vogelstein

{"title":"Inference for Multiple Heterogeneous Networks with a Common Invariant Subspace.","authors":"Jesús Arroyo, Avanti Athreya, Joshua Cape, Guodong Chen, Carey E Priebe, Joshua T Vogelstein","doi":"","DOIUrl":"","url":null,"abstract":"The development of models and methodology for the analysis of data from multiple heterogeneous networks is of importance both in statistical network theory and across a wide spectrum of application domains. Although single-graph analysis is well-studied, multiple graph inference is largely unexplored, in part because of the challenges inherent in appropriately modeling graph differences and yet retaining sufficient model simplicity to render estimation feasible. This paper addresses exactly this gap, by introducing a new model, the common subspace independent-edge multiple random graph model, which describes a heterogeneous collection of networks with a shared latent structure on the vertices but potentially different connectivity patterns for each graph. The model encompasses many popular network representations, including the stochastic blockmodel. The model is both flexible enough to meaningfully account for important graph differences, and tractable enough to allow for accurate inference in multiple networks. In particular, a joint spectral embedding of adjacency matrices-the multiple adjacency spectral embedding-leads to simultaneous consistent estimation of underlying parameters for each graph. Under mild additional assumptions, the estimates satisfy asymptotic normality and yield improvements for graph eigenvalue estimation. In both simulated and real data, the model and the embedding can be deployed for a number of subsequent network inference tasks, including dimensionality reduction, classification, hypothesis testing, and community detection. Specifically, when the embedding is applied to a data set of connectomes constructed through diffusion magnetic resonance imaging, the result is an accurate classification of brain scans by human subject and a meaningful determination of heterogeneity across scans of different individuals.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"22 141","pages":"1-49"},"PeriodicalIF":4.3,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8513708/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39543833","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimating Uncertainty Intervals from Collaborating Networks. 从协作网络中估算不确定性区间。

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-01-01

Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson

{"title":"Estimating Uncertainty Intervals from Collaborating Networks.","authors":"Tianhui Zhou, Yitong Li, Yuan Wu, David Carlson","doi":"","DOIUrl":"","url":null,"abstract":"Effective decision making requires understanding the uncertainty inherent in a prediction. In regression, this uncertainty can be estimated by a variety of methods; however, many of these methods are laborious to tune, generate overconfident uncertainty intervals, or lack sharpness (give imprecise intervals). We address these challenges by proposing a novel method to capture predictive distributions in regression by defining two neural networks with two distinct loss functions. Specifically, one network approximates the cumulative distribution function, and the second network approximates its inverse. We refer to this method as Collaborating Networks (CN). Theoretical analysis demonstrates that a fixed point of the optimization is at the idealized solution, and that the method is asymptotically consistent to the ground truth distribution. Empirically, learning is straightforward and robust. We benchmark CN against several common approaches on two synthetic and six real-world datasets, including forecasting A1c values in diabetic patients from electronic health records, where uncertainty is critical. In the synthetic data, the proposed approach essentially matches ground truth. In the real-world datasets, CN improves results on many performance metrics, including log-likelihood estimates, mean absolute errors, coverage estimates, and prediction interval widths.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"22 ","pages":""},"PeriodicalIF":4.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9231643/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"9138923","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Bayesian Distance Clustering. 贝叶斯距离聚类

IF 4.3 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-01-01

Leo L Duan, David B Dunson

引用次数: 0

Adversarial Monte Carlo Meta-Learning of Optimal Prediction Procedures. 最佳预测程序的对抗性蒙特卡罗元学习。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-01-01

Alex Luedtke, Incheoul Chung, Oleg Sofrygin

引用次数: 0

Empirical Bayes Matrix Factorization. 经验贝叶斯矩阵分解。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-01-01

Wei Wang, Matthew Stephens

{"title":"Empirical Bayes Matrix Factorization.","authors":"Wei Wang, Matthew Stephens","doi":"","DOIUrl":"","url":null,"abstract":"Matrix factorization methods, which include Factor analysis (FA) and Principal Components Analysis (PCA), are widely used for inferring and summarizing structure in multivariate data. Many such methods use a penalty or prior distribution to achieve sparse representations (\"Sparse FA/PCA\"), and a key question is how much sparsity to induce. Here we introduce a general Empirical Bayes approach to matrix factorization (EBMF), whose key feature is that it estimates the appropriate amount of sparsity by estimating prior distributions from the observed data. The approach is very flexible: it allows for a wide range of different prior families and allows that each component of the matrix factorization may exhibit a different amount of sparsity. The key to this flexibility is the use of a variational approximation, which we show effectively reduces fitting the EBMF model to solving a simpler problem, the so-called \"normal means\" problem. We demonstrate the benefits of EBMF with sparse priors through both numerical comparisons with competing methods and through analysis of data from the GTEx (Genotype Tissue Expression) project on genetic associations across 44 human tissues. In numerical comparisons EBMF often provides more accurate inferences than other methods. In the GTEx data, EBMF identifies interpretable structure that agrees with known relationships among human tissues. Software implementing our approach is available at https://github.com/stephenslab/flashr.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"22 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10621241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"71428598","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage. 通过灵活的经验贝叶斯收缩技术实现灵活的信号去噪。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-01-01

Zhengrong Xing, Peter Carbonetto, Matthew Stephens

{"title":"Flexible Signal Denoising via Flexible Empirical Bayes Shrinkage.","authors":"Zhengrong Xing, Peter Carbonetto, Matthew Stephens","doi":"","DOIUrl":"","url":null,"abstract":"Signal denoising-also known as non-parametric regression-is often performed through shrinkage estimation in a transformed (e.g., wavelet) domain; shrinkage in the transformed domain corresponds to smoothing in the original domain. A key question in such applications is how much to shrink, or, equivalently, how much to smooth. Empirical Bayes shrinkage methods provide an attractive solution to this problem; they use the data to estimate a distribution of underlying \"effects,\" hence automatically select an appropriate amount of shrinkage. However, most existing implementations of empirical Bayes shrinkage are less flexible than they could be-both in their assumptions on the underlying distribution of effects, and in their ability to handle heteroskedasticity-which limits their signal denoising applications. Here we address this by adopting a particularly flexible, stable and computationally convenient empirical Bayes shrinkage method and applying it to several signal denoising problems. These applications include smoothing of Poisson data and heteroskedastic Gaussian data. We show through empirical comparisons that the results are competitive with other methods, including both simple thresholding rules and purpose-built empirical Bayes procedures. Our methods are implemented in the R package smashr, \"SMoothing by Adaptive SHrinkage in R,\" available at https://www.github.com/stephenslab/smashr.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":"22 ","pages":""},"PeriodicalIF":6.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10751020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139040830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Nonparametric graphical model for counts. 计数的非参数图形模型。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2020-12-01

Arkaprava Roy, David B Dunson

引用次数: 0

Learning from Binary Multiway Data: Probabilistic Tensor Decomposition and its Statistical Optimality. 二元多路数据学习:概率张量分解及其统计最优性。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2020-07-01

Miaoyan Wang, Lexin Li

引用次数: 0