Faculty

Institution

Unit

Venue

Marten H. Wegkamp

Professor

Cornell University

Affiliations

College of Arts and Sciences

Department of Mathematics

College of Computing and Information Science

Department of Statistics and Data Science

Marten H. Wegkamp is currently serving as a College Of Arts And Sciences Professor at Cornell University.

Honors

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Experiences

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Education

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Marten H. Wegkamp

Cornell University

Professor

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statistics statistical methodology statistical inference computational statistics applied mathematics statistical models applied statistics high-dimensional statistics data science machine learning research estimation theory model selection estimation statistic machine learning mathematical optimization high-dimensional method latent modeling density estimation low-rank approximation computer science statistical learning theory classification method latent variable models sparse representations applied probability parameter estimation semi-nonparametric estimation supervised learning nonparametric regression weak convergence statistical theory convex analysis bayesian analysis gaussian process knowledge discovery regression analysis multivariate approximation dimensionality reduction reject option sample size biomedical informatics cost-sensitive machine learning regularization (mathematics) mathematical statistics levy process mixture analysis mixture distribution topic models empirical copula process linear regression computational optimization computational intelligence support vector machines kernel methods matrix factorization stochastic calculus multivariate analysis empirical copula factor model copula correlation factor regression model factor regression models latent factor regression adaptive pcr decision science numerical algorithms mathematical programming global optimization cancer discovery nature method automatic classification data classification computational learning theory sparsity oracle inequalities arbitrary estimators optimal rates

Notable Scholarly Works

Notable Federal Grants

Collaborators

Learning Large Softmax Mixtures With Warm Start Em. arXiv (Cornell University) (2024)

Bing, XinBunea, FlorentinaNiles-Weed, JonathanWegkamp, Marten

discrete mixturesmixture analysismachine learningstatistical learning theorylarge softmax mixturesmixture distributionstatistical inferencelanguage modelsstatisticsmachine learning researchlarge ai modelcomputational intelligenceapplied mathematicsmixture of expertsoftmax mixturescomputational statisticsneural network (machine learning)

Linear Discriminant Regularized Regression. arXiv (Cornell University) (2024)

Bing, XinLi, BingqingWegkamp, Marten

linear regressionlinear discriminant analysishigh-dimensional statisticsdimensionality reductionapplied statisticsstatistical learning theorysupervised learningclassification methodresponse regressionstatistical inferencestatisticsstatistical methodologymachine learning researchdata sciencehigh-dimensional methodcomputational statisticsmachine learning

SLIDE: Significant Latent Factor Interaction Discovery and Exploration Across Biological Domains. Nature Methods (2024)

Javad RahimikolluHanxi XiaoAnnaElaine RosengartAaron B. I. RosenTracy TabibPaul M. ZdinakKun HeXin BingFlorentina BuneaMarten WegkampAmanda C. PoholekAlok V. JoglekarRobert A. LafyatisJishnu Das

biological domainsquantitative biologybiomedical informaticsnature methodlatent modelingbiostatisticsbiosystemsdata sciencediscovery techniquecomputational biologymolecular informaticslatent variable modelsbioinformatics

Detecting Approximate Replicate Components of a High-Dimensional Random Vector With Latent Structure. Bernoulli (2023)

Xin BingFlorentina BuneaMarten Wegkamp

high-dimensional statisticsparallel analysisprincipal component analysislatent structurestatisticsstatistical methodologyreplicate componentsdata scienceapproximate replicate componentscomputational statisticscomputer sciencenumerical linear algebralatent modeling

Interpolating Discriminant Functions in High-Dimensional Gaussian Latent Mixtures. Biometrika (2023)

Xin BingMarten Wegkamp

high-dimensional statisticsstatistical learning theoryoptimal separating hyperplaneestimated hyperplaneclassification methodinterpolation propertystatisticsstatistical methodologymachine learning researchdata sciencehigh-dimensional methodcomputational statisticsmachine learning

Optimal Discriminant Analysis in High-Dimensional Latent Factor Models. The Annals of Statistics (2023)

Xin BingMarten Wegkamp

high-dimensional statisticsdimensionality reductionprincipal component analysisoptimal discriminant analysislatent modelingclassification methodpc-based classifierstatisticspattern recognitionstatistical methodologymachine learning researchhigh-dimensional methodsample sizecomputational statisticsmachine learningcomputer science

Essential Regression: A Generalizable Framework for Inferring Causal Latent Factors From Multi-Omic Datasets. Patterns (2022)

Xin BingTyler LovelaceFlorentina BuneaMarten WegkampSudhir Pai KasturiHarinder SinghPanayiotis V. BenosJishnu Das

immunologymolecular inferencesbiomedical informaticsomicsomics integrationsystems biologycausal latent factorsmultiomicsomics datasetsessential regressionbiostatisticsmachine learning researchmulti-omics studiesbioinformatics

Inference in Latent Factor Regression With Clusterable Features. Bernoulli (2022)

Xin BingFlorentina BuneaMarten Wegkamp

latent factor regressionclusterable featuresapplied statisticslatent modelingstatistical inferencestatisticsstatistical methodologydata sciencefactor regression modelscomputational statisticslatent variable modelsstatistical models

Likelihood Estimation of Sparse Topic Distributions in Topic Models and Its Applications to Wasserstein Document Distance Calculations. The Annals of Statistics (2022)

Xin BingFlorentina BuneaSeth Strimas-MackeyMarten Wegkamp

mixture analysisdocument clusteringlikelihood estimationhigh-dimensional statisticstext miningstatistical inferencenatural language processingstatisticsstatistical methodologyknowledge discoverydata sciencemachine learning researchtopic modelssparse topic distributionscomputational statisticsmixture distributionstatistical models

Inference in Interpretable Latent Factor Regression Models. Bernoulli (2021)

Xin BingFlorentina Bunea

interpretabilitylatent modelingstatistical inferencebayesian analysisstatisticsinferencestatistical methodologycomputational statisticsmodel selectionlatent variable modelsstatistical models

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Marten H. Wegkamp

Professor

Cornell University

Affiliations

College of Arts and Sciences

Department of Mathematics

College of Computing and Information Science

Department of Statistics and Data Science

Marten H. Wegkamp is currently serving as a College Of Arts And Sciences Professor at Cornell University.

Honors

Create free account to unlock more features

Experiences

Create free account to unlock more features

Education

Create free account to unlock more features

Marten H. Wegkamp

Cornell University

Professor

You can click a keyword to filter the displayed items

Notable Scholarly Works

Notable Federal Grants

Collaborators

Learning Large Softmax Mixtures With Warm Start Em. arXiv (Cornell University) (2024)

Bing, XinBunea, FlorentinaNiles-Weed, JonathanWegkamp, Marten

Linear Discriminant Regularized Regression. arXiv (Cornell University) (2024)

Bing, XinLi, BingqingWegkamp, Marten

SLIDE: Significant Latent Factor Interaction Discovery and Exploration Across Biological Domains. Nature Methods (2024)

Javad RahimikolluHanxi XiaoAnnaElaine RosengartAaron B. I. RosenTracy TabibPaul M. ZdinakKun HeXin BingFlorentina BuneaMarten WegkampAmanda C. PoholekAlok V. JoglekarRobert A. LafyatisJishnu Das

Detecting Approximate Replicate Components of a High-Dimensional Random Vector With Latent Structure. Bernoulli (2023)

Xin BingFlorentina BuneaMarten Wegkamp

Interpolating Discriminant Functions in High-Dimensional Gaussian Latent Mixtures. Biometrika (2023)

Xin BingMarten Wegkamp

Optimal Discriminant Analysis in High-Dimensional Latent Factor Models. The Annals of Statistics (2023)

Xin BingMarten Wegkamp

Essential Regression: A Generalizable Framework for Inferring Causal Latent Factors From Multi-Omic Datasets. Patterns (2022)

Xin BingTyler LovelaceFlorentina BuneaMarten WegkampSudhir Pai KasturiHarinder SinghPanayiotis V. BenosJishnu Das

Inference in Latent Factor Regression With Clusterable Features. Bernoulli (2022)

Xin BingFlorentina BuneaMarten Wegkamp

Likelihood Estimation of Sparse Topic Distributions in Topic Models and Its Applications to Wasserstein Document Distance Calculations. The Annals of Statistics (2022)

Xin BingFlorentina BuneaSeth Strimas-MackeyMarten Wegkamp

Inference in Interpretable Latent Factor Regression Models. Bernoulli (2021)

Xin BingFlorentina Bunea

interpretabilitylatent modelingstatistical inferencebayesian analysisstatisticsinferencestatistical methodologycomputational statisticsmodel selectionlatent variable modelsstatistical models

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