Journal of Machine Learning Research最新文献_第9页

SnapVX: A Network-Based Convex Optimization Solver. 基于网络的凸优化求解器。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2017-01-01

David Hallac, Christopher Wong, Steven Diamond, Abhijit Sharang, Rok Sosič, Stephen Boyd, Jure Leskovec

引用次数: 0

Bridging supervised learning and test-based co-optimization 桥梁监督学习和基于测试的协同优化

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2017-01-01 DOI: 10.5555/3122009.3122047

PopoviciElena

引用次数: 0

The DFS fused lasso DFS熔接套索

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2017-01-01 DOI: 10.5555/3122009.3242033

PadillaOscar Hernan Madrid, SharpnackJames, G. ScottJames

引用次数: 0

Structure-Leveraged Methods in Breast Cancer Risk Prediction. 乳腺癌风险预测中的结构杠杆方法。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-12-01

Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M Ong, Peggy Peissig, Elizabeth Burnside

{"title":"Structure-Leveraged Methods in Breast Cancer Risk Prediction.","authors":"Jun Fan, Yirong Wu, Ming Yuan, David Page, Jie Liu, Irene M Ong, Peggy Peissig, Elizabeth Burnside","doi":"","DOIUrl":"","url":null,"abstract":"Predicting breast cancer risk has long been a goal of medical research in the pursuit of precision medicine. The goal of this study is to develop novel penalized methods to improve breast cancer risk prediction by leveraging structure information in electronic health records. We conducted a retrospective case-control study, garnering 49 mammography descriptors and 77 high-frequency/low-penetrance single-nucleotide polymorphisms (SNPs) from an existing personalized medicine data repository. Structured mammography reports and breast imaging features have long been part of a standard electronic health record (EHR), and genetic markers likely will be in the near future. Lasso and its variants are widely used approaches to integrated learning and feature selection, and our methodological contribution is to incorporate the dependence structure among the features into these approaches. More specifically, we propose a new methodology by combining group penalty and [Formula: see text] (1 ≤ p ≤ 2) fusion penalty to improve breast cancer risk prediction, taking into account structure information in mammography descriptors and SNPs. We demonstrate that our method provides benefits that are both statistically significant and potentially significant to people's lives.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2016-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5446896/pdf/nihms-826646.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"35042470","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

Double or Nothing 要么加倍要么一无所获

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-08-24 DOI: 10.5555/2946645.3053447

Carol Sutton

引用次数: 0

Convex Regression with Interpretable Sharp Partitions. 带可解释锐分区的凸回归

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-06-01

Ashley Petersen, Noah Simon, Daniela Witten

引用次数: 0

L₁-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs. 高斯设计的高维单指数模型支持恢复的 L1-Regularized Least Squares。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-05-01

Matey Neykov, Jun S Liu, Tianxi Cai

{"title":"L1-Regularized Least Squares for Support Recovery of High Dimensional Single Index Models with Gaussian Designs.","authors":"Matey Neykov, Jun S Liu, Tianxi Cai","doi":"","DOIUrl":"","url":null,"abstract":"It is known that for a certain class of single index models (SIMs) [Formula: see text], support recovery is impossible when X ~ 𝒩(0, 𝕀 p×p ) and a model complexity adjusted sample size is below a critical threshold. Recently, optimal algorithms based on Sliced Inverse Regression (SIR) were suggested. These algorithms work provably under the assumption that the design X comes from an i.i.d. Gaussian distribution. In the present paper we analyze algorithms based on covariance screening and least squares with L1 penalization (i.e. LASSO) and demonstrate that they can also enjoy optimal (up to a scalar) rescaled sample size in terms of support recovery, albeit under slightly different assumptions on f and ε compared to the SIR based algorithms. Furthermore, we show more generally, that LASSO succeeds in recovering the signed support of β0 if X ~ 𝒩 (0, Σ), and the covariance Σ satisfies the irrepresentable condition. Our work extends existing results on the support recovery of LASSO for the linear model, to a more general class of SIMs.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2016-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5426818/pdf/nihms851690.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34994441","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

CVXPY: A Python-Embedded Modeling Language for Convex Optimization. 一种用于凸优化的python嵌入式建模语言。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-04-01

Steven Diamond, Stephen Boyd

引用次数: 0

A Gibbs Sampler for Learning DAGs. 学习 DAG 的 Gibbs 采样器

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-04-01

Robert J B Goudie, Sach Mukherjee

引用次数: 0

On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint. 数据稀疏性约束下核希尔伯特空间再现的分位数回归。

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2016-04-01

Chong Zhang, Yufeng Liu, Yichao Wu

{"title":"On Quantile Regression in Reproducing Kernel Hilbert Spaces with Data Sparsity Constraint.","authors":"Chong Zhang, Yufeng Liu, Yichao Wu","doi":"","DOIUrl":"","url":null,"abstract":"For spline regressions, it is well known that the choice of knots is crucial for the performance of the estimator. As a general learning framework covering the smoothing splines, learning in a Reproducing Kernel Hilbert Space (RKHS) has a similar issue. However, the selection of training data points for kernel functions in the RKHS representation has not been carefully studied in the literature. In this paper we study quantile regression as an example of learning in a RKHS. In this case, the regular squared norm penalty does not perform training data selection. We propose a data sparsity constraint that imposes thresholding on the kernel function coefficients to achieve a sparse kernel function representation. We demonstrate that the proposed data sparsity method can have competitive prediction performance for certain situations, and have comparable performance in other cases compared to that of the traditional squared norm penalty. Therefore, the data sparsity method can serve as a competitive alternative to the squared norm penalty method. Some theoretical properties of our proposed method using the data sparsity constraint are obtained. Both simulated and real data sets are used to demonstrate the usefulness of our data sparsity constraint.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2016-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4850041/pdf/nihms729829.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"34446126","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