Journal of Machine Learning Research最新文献_第6页

Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach. 高维广义线性模型的估计与推理:一种分裂与平滑方法。

IF 6 3区计算机科学

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

Zhe Fei, Yi Li

{"title":"Estimation and Inference for High Dimensional Generalized Linear Models: A Splitting and Smoothing Approach.","authors":"Zhe Fei, Yi Li","doi":"","DOIUrl":"","url":null,"abstract":"The focus of modern biomedical studies has gradually shifted to explanation and estimation of joint effects of high dimensional predictors on disease risks. Quantifying uncertainty in these estimates may provide valuable insight into prevention strategies or treatment decisions for both patients and physicians. High dimensional inference, including confidence intervals and hypothesis testing, has sparked much interest. While much work has been done in the linear regression setting, there is lack of literature on inference for high dimensional generalized linear models. We propose a novel and computationally feasible method, which accommodates a variety of outcome types, including normal, binomial, and Poisson data. We use a \"splitting and smoothing\" approach, which splits samples into two parts, performs variable selection using one part and conducts partial regression with the other part. Averaging the estimates over multiple random splits, we obtain the smoothed estimates, which are numerically stable. We show that the estimates are consistent, asymptotically normal, and construct confidence intervals with proper coverage probabilities for all predictors. We examine the finite sample performance of our method by comparing it with the existing methods and applying it to analyze a lung cancer cohort study.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8442657/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39443931","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

Quantile Graphical Models: Bayesian Approaches. 分位数图形模型:贝叶斯方法。

IF 6 3区计算机科学

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

Nilabja Guha, Veera Baladandayuthapani, Bani K Mallick

引用次数: 0

Near-optimal Individualized Treatment Recommendations. 近乎最佳的个体化治疗建议。

IF 6 3区计算机科学

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

Haomiao Meng, Ying-Qi Zhao, Haoda Fu, Xingye Qiao

{"title":"Near-optimal Individualized Treatment Recommendations.","authors":"Haomiao Meng, Ying-Qi Zhao, Haoda Fu, Xingye Qiao","doi":"","DOIUrl":"","url":null,"abstract":"The individualized treatment recommendation (ITR) is an important analytic framework for precision medicine. The goal of ITR is to assign the best treatments to patients based on their individual characteristics. From the machine learning perspective, the solution to the ITR problem can be formulated as a weighted classification problem to maximize the mean benefit from the recommended treatments given patients' characteristics. Several ITR methods have been proposed in both the binary setting and the multicategory setting. In practice, one may prefer a more flexible recommendation that includes multiple treatment options. This motivates us to develop methods to obtain a set of near-optimal individualized treatment recommendations alternative to each other, called alternative individualized treatment recommendations (A-ITR). We propose two methods to estimate the optimal A-ITR within the outcome weighted learning (OWL) framework. Simulation studies and a real data analysis for Type 2 diabetic patients with injectable antidiabetic treatments are conducted to show the usefulness of the proposed A-ITR framework. We also show the consistency of these methods and obtain an upper bound for the risk between the theoretically optimal recommendation and the estimated one. An R package aitr has been developed, found at https://github.com/menghaomiao/aitr.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8324003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39264728","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

Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions. 鲁棒异步随机梯度推:强凸函数的渐近最优和网络无关性能。

IF 6 3区计算机科学

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

Artin Spiridonoff, Alex Olshevsky, Ioannis Ch Paschalidis

{"title":"Robust Asynchronous Stochastic Gradient-Push: Asymptotically Optimal and Network-Independent Performance for Strongly Convex Functions.","authors":"Artin Spiridonoff, Alex Olshevsky, Ioannis Ch Paschalidis","doi":"","DOIUrl":"","url":null,"abstract":"We consider the standard model of distributed optimization of a sum of functions <math><mrow><mi>F</mi> <mrow><mo>(</mo> <mi>z</mi> <mo>)</mo></mrow> <mo>=</mo> <msubsup><mo>∑</mo> <mrow><mi>i</mi> <mo>=</mo> <mn>1</mn></mrow> <mi>n</mi></msubsup> <mrow><msub><mi>f</mi> <mi>i</mi></msub> <mrow><mo>(</mo> <mi>z</mi> <mo>)</mo></mrow> </mrow> </mrow> </math> , where node i in a network holds the function fi (z). We allow for a harsh network model characterized by asynchronous updates, message delays, unpredictable message losses, and directed communication among nodes. In this setting, we analyze a modification of the Gradient-Push method for distributed optimization, assuming that (i) node i is capable of generating gradients of its function fi (z) corrupted by zero-mean bounded-support additive noise at each step, (ii) F(z) is strongly convex, and (iii) each fi (z) has Lipschitz gradients. We show that our proposed method asymptotically performs as well as the best bounds on centralized gradient descent that takes steps in the direction of the sum of the noisy gradients of all the functions f 1(z), …, fn (z) at each step.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520166/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38434192","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

A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models. 高维广义线性模型的基于正则化的自适应检验。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2020-01-01 Epub Date: 2020-07-26

Chong Wu, Gongjun Xu, Xiaotong Shen, Wei Pan

{"title":"A Regularization-Based Adaptive Test for High-Dimensional Generalized Linear Models.","authors":"Chong Wu, Gongjun Xu, Xiaotong Shen, Wei Pan","doi":"","DOIUrl":"","url":null,"abstract":"In spite of its urgent importance in the era of big data, testing high-dimensional parameters in generalized linear models (GLMs) in the presence of high-dimensional nuisance parameters has been largely under-studied, especially with regard to constructing powerful tests for general (and unknown) alternatives. Most existing tests are powerful only against certain alternatives and may yield incorrect Type I error rates under high-dimensional nuisance parameter situations. In this paper, we propose the adaptive interaction sum of powered score (aiSPU) test in the framework of penalized regression with a non-convex penalty, called truncated Lasso penalty (TLP), which can maintain correct Type I error rates while yielding high statistical power across a wide range of alternatives. To calculate its p-values analytically, we derive its asymptotic null distribution. Via simulations, its superior finite-sample performance is demonstrated over several representative existing methods. In addition, we apply it and other representative tests to an Alzheimer's Disease Neuroimaging Initiative (ADNI) data set, detecting possible gene-gender interactions for Alzheimer's disease. We also put R package \"aispu\" implementing the proposed test on GitHub.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7425805/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38270305","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

Minimax Nonparametric Parallelism Test. 最小非参数平行检验。

IF 6 3区计算机科学

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

Xin Xing, Meimei Liu, Ping Ma, Wenxuan Zhong

引用次数: 0

Provable Convex Co-clustering of Tensors. 可证明的张量凸共聚

IF 4.3 3区计算机科学

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

Eric C Chi, Brian R Gaines, Will Wei Sun, Hua Zhou, Jian Yang

{"title":"Provable Convex Co-clustering of Tensors.","authors":"Eric C Chi, Brian R Gaines, Will Wei Sun, Hua Zhou, Jian Yang","doi":"","DOIUrl":"","url":null,"abstract":"Cluster analysis is a fundamental tool for pattern discovery of complex heterogeneous data. Prevalent clustering methods mainly focus on vector or matrix-variate data and are not applicable to general-order tensors, which arise frequently in modern scientific and business applications. Moreover, there is a gap between statistical guarantees and computational efficiency for existing tensor clustering solutions due to the nature of their non-convex formulations. In this work, we bridge this gap by developing a provable convex formulation of tensor co-clustering. Our convex co-clustering (CoCo) estimator enjoys stability guarantees and its computational and storage costs are polynomial in the size of the data. We further establish a non-asymptotic error bound for the CoCo estimator, which reveals a surprising \"blessing of dimensionality\" phenomenon that does not exist in vector or matrix-variate cluster analysis. Our theoretical findings are supported by extensive simulated studies. Finally, we apply the CoCo estimator to the cluster analysis of advertisement click tensor data from a major online company. Our clustering results provide meaningful business insights to improve advertising effectiveness.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2020-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7731944/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38706545","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

Generalized Score Matching for Non-Negative Data. 非负数据的广义分数匹配。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2019-04-01

Shiqing Yu, Mathias Drton, Ali Shojaie

引用次数: 0