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

Estimating Uncertainty Intervals from Collaborating Networks. 估算协作网络的不确定性区间。

IF 6 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":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/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

Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data. 混合多视图数据的综合广义凸聚类优化与特征选择。

IF 6 3区计算机科学

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

Minjie Wang, Genevera I Allen

{"title":"Integrative Generalized Convex Clustering Optimization and Feature Selection for Mixed Multi-View Data.","authors":"Minjie Wang, Genevera I Allen","doi":"","DOIUrl":"","url":null,"abstract":"In mixed multi-view data, multiple sets of diverse features are measured on the same set of samples. By integrating all available data sources, we seek to discover common group structure among the samples that may be hidden in individualistic cluster analyses of a single data view. While several techniques for such integrative clustering have been explored, we propose and develop a convex formalization that enjoys strong empirical performance and inherits the mathematical properties of increasingly popular convex clustering methods. Specifically, our Integrative Generalized Convex Clustering Optimization (iGecco) method employs different convex distances, losses, or divergences for each of the different data views with a joint convex fusion penalty that leads to common groups. Additionally, integrating mixed multi-view data is often challenging when each data source is high-dimensional. To perform feature selection in such scenarios, we develop an adaptive shifted group-lasso penalty that selects features by shrinking them towards their loss-specific centers. Our so-called iGecco+ approach selects features from each data view that are best for determining the groups, often leading to improved integrative clustering. To solve our problem, we develop a new type of generalized multi-block ADMM algorithm using sub-problem approximations that more efficiently fits our model for big data sets. Through a series of numerical experiments and real data examples on text mining and genomics, we show that iGecco+ achieves superior empirical performance for high-dimensional mixed multi-view data.","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/PMC8570363/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39596948","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 6 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

Inference for the Case Probability in High-dimensional Logistic Regression. 高维逻辑回归中的案例概率推断。

IF 6 3区计算机科学

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

Zijian Guo, Prabrisha Rakshit, Daniel S Herman, Jinbo Chen

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":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/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

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

Estimation and Optimization of Composite Outcomes. 综合结果的估算和优化。

IF 4.3 3区计算机科学

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

Daniel J Luckett, Eric B Laber, Siyeon Kim, Michael R Kosorok

{"title":"Estimation and Optimization of Composite Outcomes.","authors":"Daniel J Luckett, Eric B Laber, Siyeon Kim, Michael R Kosorok","doi":"","DOIUrl":"","url":null,"abstract":"There is tremendous interest in precision medicine as a means to improve patient outcomes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it maximizes the mean of a scalar outcome in a population of interest, e.g., symptom reduction. However, clinical and intervention scientists often seek to balance multiple and possibly competing outcomes, e.g., symptom reduction and the risk of an adverse event. One approach to precision medicine in this setting is to elicit a composite outcome which balances all competing outcomes; unfortunately, eliciting a composite outcome directly from patients is difficult without a high-quality instrument, and an expert-derived composite outcome may not account for heterogeneity in patient preferences. We propose a new paradigm for the study of precision medicine using observational data that relies solely on the assumption that clinicians are approximately (i.e., imperfectly) making decisions to maximize individual patient utility. Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite outcomes. The estimated composite outcomes and estimated optimal individualized treatment rule provide new insights into patient preference heterogeneity, clinician behavior, and the value of precision medicine in a given domain. We derive inference procedures for the proposed estimators under mild conditions and demonstrate their finite sample performance through a suite of simulation experiments and an illustrative application to data from a study of bipolar depression.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":4.3,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8562677/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39588763","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

LDLE: Low Distortion Local Eigenmaps. LDLE：低失真局部特征图。

IF 4.3 3区计算机科学

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

Dhruv Kohli, Alexander Cloninger, Gal Mishne

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":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/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

Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation. 使用最大似然估计的时变mdp学习和规划。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2021-01-01 Epub Date: 2021-02-01

Melkior Ornik, Ufuk Topcu

{"title":"Learning and Planning for Time-Varying MDPs Using Maximum Likelihood Estimation.","authors":"Melkior Ornik, Ufuk Topcu","doi":"","DOIUrl":"","url":null,"abstract":"This paper proposes a formal approach to online learning and planning for agents operating in a priori unknown, time-varying environments. The proposed method computes the maximally likely model of the environment, given the observations about the environment made by an agent earlier in the system run and assuming knowledge of a bound on the maximal rate of change of system dynamics. Such an approach generalizes the estimation method commonly used in learning algorithms for unknown Markov decision processes with time-invariant transition probabilities, but is also able to quickly and correctly identify the system dynamics following a change. Based on the proposed method, we generalize the exploration bonuses used in learning for time-invariant Markov decision processes by introducing a notion of uncertainty in a learned time-varying model, and develop a control policy for time-varying Markov decision processes based on the exploitation and exploration trade-off. We demonstrate the proposed methods on four numerical examples: a patrolling task with a change in system dynamics, a two-state MDP with periodically changing outcomes of actions, a wind flow estimation task, and a multi-armed bandit problem with periodically changing probabilities of different rewards.","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/PMC8739185/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39913174","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