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

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

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":"21 ","pages":""},"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":"21 ","pages":""},"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