Efficient discovery of overlapping communities in massive networksTopographic factor analysis: a Bayesian model for inferring brain networks from neural dataLatent Dirichlet AllocationReading Tea Leaves: How Humans Interpret Topic ModelsDeep learning with hierarchical convolutional factor analysisRisk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis.A topographic latent source model for fMRI data.Decomposing spatiotemporal brain patterns into topographic latent sourcesMixed Membership Stochastic BlockmodelsPosterior predictive checks to quantify lack-of-fit in admixture models of latent population structure.Context, learning, and extinction.Science and data science.Scaling probabilistic models of genetic variation to millions of humans.A Bayesian Nonparametric Approach to Image Super-Resolution.Distance Dependent Infinite Latent Feature Models.Probabilistic Topic Models: A focus on graphical model design and applications to document and image analysis.Structured Embedding Models for Grouped DataVariational Inference via χ Upper Bound MinimizationContext Selection for Embedding ModelsHierarchical Implicit Models and Likelihood-Free Variational InferenceThe Generalized Reparameterization GradientExponential Family EmbeddingsOperator Variational InferenceA probabilistic approach to discovering dynamic full-brain functional connectivity patternsNested Hierarchical Dirichlet Processes.Measuring discursive influence across scholarship.Readmission prediction via deep contextual embedding of clinical concepts.Probabilistic topic modelsA correlated topic model of ScienceExploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts fundingCombining Stochastic Block Models and Mixed Membership for Statistical Network AnalysisA latent mixed membership model for relational dataScaling probabilistic models of genetic variation to millions of humansConnections between the linesPoisson-Randomized Gamma Dynamical SystemsAdapting Neural Networks for the Estimation of Treatment EffectsVariational Bayes under Model MisspecificationUsing Embeddings to Correct for Unobserved Confounding in NetworksAutomatic Variational Inference in StanThe Population Posterior and Bayesian Modeling on Streams
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American artificial intelligence researcher
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David Blei
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