Yuan Huang, PhD
Assistant Professor of Biostatistics (Biostatistics)Cards
About
Research
Publications
2024
Deep Dimension Reduction for Supervised Representation Learning
Huang J, Jiao Y, Liao X, Liu J, Yu Z. Deep Dimension Reduction for Supervised Representation Learning. IEEE Transactions On Information Theory 2024, 70: 3583-3598. DOI: 10.1109/tit.2023.3340658.Peer-Reviewed Original ResearchRepresentation learningStandard deep learning modelsHigh-dimensional complex dataSupervised representation learningRepresentation learning tasksDeep neural networksEffective data representationsContext of classificationDeep learning modelsNonparametric representationDimension reduction methodDimension reduction approachLearned representationsPromote disentanglementData representationNeural networkComplex dataLearning modelsDimension reductionTarget representationLearning tasksReduction methodSufficient dimension reduction methodsLow-dimensionalConditional independence
2023
Online inference in high-dimensional generalized linear models with streaming data.
Luo L, Han R, Lin Y, Huang J. Online inference in high-dimensional generalized linear models with streaming data. Electronic Journal Of Statistics 2023, 17: 3443-3471. PMID: 39188774, PMCID: PMC11346802, DOI: 10.1214/23-ejs2182.Peer-Reviewed Original ResearchOnline inference with debiased stochastic gradient descent
Han R, Luo L, Lin Y, Huang J. Online inference with debiased stochastic gradient descent. Biometrika 2023, 111: 93-108. DOI: 10.1093/biomet/asad046.Peer-Reviewed Original ResearchStochastic gradient descent algorithmHigh-dimensional statisticsOne-pass algorithmGradient descent algorithmHigh-dimensional dataAsymptotic normalityText datasetsSparsity levelOnline fashionOnline inferenceData distributionTime complexitySpace complexityDescent algorithmStatistical inferenceUpdate stepNumerical experimentsAlgorithmDebiasing techniquesMild conditionsInferenceSparsityEstimationConfidence intervalsDatasetHETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES.
Zhong T, Zhang Q, Huang J, Wu M, Ma S. HETEROGENEITY ANALYSIS VIA INTEGRATING MULTI-SOURCES HIGH-DIMENSIONAL DATA WITH APPLICATIONS TO CANCER STUDIES. Statistica Sinica 2023, 33: 729-758. PMID: 38037567, PMCID: PMC10686523, DOI: 10.5705/ss.202021.0002.Peer-Reviewed Original Research
2021
Regularized projection score estimation of treatment effects in high-dimensional quantile regression
Cheng C, Feng X, Huang J, Liu X. Regularized projection score estimation of treatment effects in high-dimensional quantile regression. Statistica Sinica 2021 DOI: 10.5705/ss.202019.0247.Peer-Reviewed Original Research
2017
A group adaptive elastic-net approach for variable selection in high-dimensional linear regression
Hu J, Huang J, Qiu F. A group adaptive elastic-net approach for variable selection in high-dimensional linear regression. Science China Mathematics 2017, 61: 173-188. DOI: 10.1007/s11425-016-0071-x.Peer-Reviewed Original ResearchAdaptive elastic-netHigh-dimensional linear regressionProblem of group selectionElastic-netOracle propertyOracle inequalitiesHigh-dimensional problemsVariable selectionGroup structureSample sizeModel selectionCollinearity problemElastic netOracleElastic-net approachHigh-dimensionalCompetitive methodsData studiesLinear regression modelsProblemInequalityModel consistencyGroup numberStatistical modelInference
2015
Asymptotic properties of Lasso in high-dimensional partially linear models
Ma C, Huang J. Asymptotic properties of Lasso in high-dimensional partially linear models. Science China Mathematics 2015, 59: 769-788. DOI: 10.1007/s11425-015-5093-2.Peer-Reviewed Original ResearchHigh-dimensional partially linear modelsPartially linear modelsLinear partPerformance of variable selectionFinite sample performanceNonparametric function estimationRate of convergenceTruncated series expansionNonparametric componentAsymptotic propertiesNonparametric functionOracle inequalitiesRegularity conditionsSufficient conditionsLasso estimatorPolynomial splinesFunction estimationSparsity assumptionLinear modelVariable selectionSimulation studySeries expansionEstimation errorRegression coefficientsLinear component