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

Empirical evaluation of resampling procedures for optimising SVM hyperparameters 优化支持向量机超参数的重采样过程的经验评价

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2017-01-01 DOI: 10.5555/3122009.3122024

WainerJacques, CawleyGavin

引用次数: 0

Community Extraction in Multilayer Networks with Heterogeneous Community Structure. 具有异构社区结构的多层网络中的社区提取。

IF 6 3区计算机科学

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

James D Wilson, John Palowitch, Shankar Bhamidi, Andrew B Nobel

{"title":"Community Extraction in Multilayer Networks with Heterogeneous Community Structure.","authors":"James D Wilson, John Palowitch, Shankar Bhamidi, Andrew B Nobel","doi":"","DOIUrl":"","url":null,"abstract":"Multilayer networks are a useful way to capture and model multiple, binary or weighted relationships among a fixed group of objects. While community detection has proven to be a useful exploratory technique for the analysis of single-layer networks, the development of community detection methods for multilayer networks is still in its infancy. We propose and investigate a procedure, called Multilayer Extraction, that identifies densely connected vertex-layer sets in multilayer networks. Multilayer Extraction makes use of a significance based score that quantifies the connectivity of an observed vertex-layer set through comparison with a fixed degree random graph model. Multilayer Extraction directly handles networks with heterogeneous layers where community structure may be different from layer to layer. The procedure can capture overlapping communities, as well as background vertex-layer pairs that do not belong to any community. We establish consistency of the vertex-layer set optimizer of our proposed multilayer score under the multilayer stochastic block model. We investigate the performance of Multilayer Extraction on three applications and a test bed of simulations. Our theoretical and numerical evaluations suggest that Multilayer Extraction is an effective exploratory tool for analyzing complex multilayer networks. Publicly available code is available at https://github.com/jdwilson4/MultilayerExtraction.","PeriodicalId":50161,"journal":{"name":"Journal of Machine Learning Research","volume":null,"pages":null},"PeriodicalIF":6.0,"publicationDate":"2017-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6927681/pdf/nihms-1022819.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"37486356","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

Automatic differentiation in machine learning 机器学习中的自动微分

IF 6 3区计算机科学

Journal of Machine Learning Research Pub Date : 2017-01-01 DOI: 10.5555/3122009.3242010

BaydinAtılım Günes, A. PearlmutterBarak, RadulAlexey Andreyevich, S. Mark

引用次数: 23

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