David M. Blei

发表

Equal Opportunity and Affirmative Action via Counterfactual Predictions pdf

Yixin Wang, Dhanya Sridhar, David M. Blei, 2019, ArXiv.

Applications of latent variable models in modeling influence and decision making

Sean Gerrish, David M. Blei, D. Blei, 2013 .

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems

Scott W. Linderman, Ryan P. Adams, Matthew J. Johnson, 2017, AISTATS.

Using Embeddings to Correct for Unobserved Confounding

David M. Blei, Yixin Wang, Victor Veitch, 2019, NeurIPS.

Data-Driven Recomposition using the Hierarchical Dirichlet Process Hidden Markov Model

Perry R. Cook, David M. Blei, Matthew D. Hoffman, 2009, ICMC.

Proceedings of the 2006 conference on Statistical network analysis

Edoardo M. Airoldi, Eric P. Xing, Stephen E. Fienberg, 2006 .

Supervised Topic Models

David M. Blei, Jon D. McAuliffe, Jon D. McAuliffe, 2007, NIPS.

Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable

Warren B. Powell, David M. Blei, Lauren Hannah, 2010, NIPS.

Frequentist Consistency of Variational Bayes

David M. Blei, Yixin Wang, D. Blei, 2017, Journal of the American Statistical Association.

Hierarchical relational models for document networks

David M. Blei, Jonathan Chang, D. Blei, 2009, 0909.4331.

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

David M. Blei, Mingyuan Zhou, Hanna M. Wallach, 2016, ICML.

A Language-based Approach to Measuring Scholarly Impact

Sean Gerrish, David M. Blei, D. Blei, 2010, ICML.

Deep learning?

Guillermo Sapiro, Lawrence Carin, David M. Blei, 1999 .

Continuous Time Dynamic Topic Models

Chong Wang, David Heckerman, David M. Blei, 2008, UAI.

PUTOP: Turning Predominant Senses into a Topic Model for Word Sense Disambiguation

David M. Blei, Jordan Boyd-Graber, 2007, SemEval@ACL.

Context Selection for Embedding Models

David M. Blei, Francisco J. R. Ruiz, Susan Athey, 2017, NIPS.

Distance Dependent Infinite Latent Feature Models

Peter I. Frazier, David M. Blei, Samuel Gershman, 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Automatic Variational Inference in Stan

Andrew Gelman, David M. Blei, Rajesh Ranganath, 2015, NIPS.

A Probabilistic Model for Using Social Networks in Personalized Item Recommendation

Tina Eliassi-Rad, David M. Blei, Allison June-Barlow Chaney, 2015, RecSys.

A Bayesian Analysis of Dynamics in Free Recall

Adler J. Perotte, Richard Socher, David M. Blei, 2009, NIPS.

Variational Inference for Adaptor Grammars

Noah A. Smith, Shay B. Cohen, David M. Blei, 2010, NAACL.

Multilingual Topic Models for Unaligned Text pdf

David M. Blei, Jordan L. Boyd-Graber, Jordan Boyd-Graber, 2009, UAI.

Probabilistic models of text and images

Michael I. Jordan, David M. Blei, D. Blei, 2004 .

32nd International Conference on Machine Learning : (ICML 2015) : Lile, France, 6-11 July 2015

David M. Blei, Francis Bach, F. Bach, 2016 .

Predicting Legislative Roll Calls from Text

Sean Gerrish, David M. Blei, D. Blei, 2011, ICML.

PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks

Miroslav Dudík, David M. Blei, Jonathan Chang, 2007, Fourth International Workshop on Semantic Evaluations (SemEval-2007).

Bayesian Spectral Matching: Turning Young MC into MC Hammer via MCMC Sampling

Perry R. Cook, David M. Blei, Matthew D. Hoffman, 2009, ICMC.

Comment: A Discussion of “Nonparametric Bayes Modeling of Populations of Networks”

Scott W. Linderman, David M. Blei, D. Blei, 2017 .

How They Vote: Issue-Adjusted Models of Legislative Behavior

Sean Gerrish, David M. Blei, D. Blei, 2012, NIPS.

Stochastic Block Models of Mixed Membership

Edoardo M. Airoldi, Eric P. Xing, Stephen E. Fienberg, 2006 .

Detecting and Characterizing Events

David M. Blei, Hanna M. Wallach, Allison June-Barlow Chaney, 2016, EMNLP.

The Survival Filter: Joint Survival Analysis with a Latent Time Series

Adler J. Perotte, Noémie Elhadad, David M. Blei, 2015, UAI.

Nonparametric Bayes Pachinko Allocation

Wei Li, Andrew McCallum, David M. Blei, 2007, UAI.

Population Predictive Checks pdf

David M. Blei, Rajesh Ranganath, D. Blei, 2019, ArXiv.

Hierarchical maximum entropy density estimation

Miroslav Dudík, Robert E. Schapire, David M. Blei, 2007, ICML '07.

Equation Embeddings pdf

David M. Blei, Kriste Krstovski, D. Blei, 2018, ArXiv.

Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis

Edoardo M. Airoldi, Eric P. Xing, Stephen E. Fienberg, 2006, SNA@ICML.

Hierarchical Topic Models and the Nested Chinese Restaurant Process

Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum, 2003, NIPS.

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis

Adler J. Perotte, Noémie Elhadad, David M. Blei, 2015, J. Am. Medical Informatics Assoc..

Integrating Topics and Syntax

Thomas L. Griffiths, Joshua B. Tenenbaum, David M. Blei, 2004, NIPS.

Surveying a suite of algorithms that offer a solution to managing large document archives.

David M. Blei, D. Blei, 2012 .

Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process

Chong Wang, David M. Blei, D. Blei, 2009, NIPS.

Markov Topic Models

Bo Thiesson, Chong Wang, David M. Blei, 2009, AISTATS.

Variational inference and learning for a unified model of syntax, semantics and morphology

David M. Blei, John Lafferty, Leonid Kontorovich, 2006 .

A Probabilistic Model of Cardiac Physiology and Electrocardiograms pdf

David M. Blei, John P. Cunningham, Andrew C. Miller, 2018, ArXiv.

Stochastic Structured Variational Inference

David M. Blei, Matthew D. Hoffman, D. Blei, 2014, AISTATS.

The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs) pdf

George Hripcsak, Yixin Wang, David M. Blei, 2019, ArXiv.

Online Variational Inference for the Hierarchical Dirichlet Process

Chong Wang, David M. Blei, John W. Paisley, 2011, AISTATS.

Stochastic variational inference pdf

Chong Wang, David M. Blei, Matthew D. Hoffman, 2012, J. Mach. Learn. Res..

Dynamic and Supervised Topic Models for Literature-Based Discovery

David M. Blei, D. Blei, 2011 .

The Dynamic Embedded Topic Model pdf

David M. Blei, Francisco J. R. Ruiz, Adji B. Dieng, 2019, ArXiv.

Distance dependent Chinese restaurant processes

Peter I. Frazier, David M. Blei, P. Frazier, 2009, ICML.

Causal Inference for Recommender Systems

David M. Blei, Yixin Wang, Dawen Liang, 2020, RecSys.

Structured Stochastic Variational Inference

David M. Blei, Matthew D. Hoffman, D. Blei, 2014, 1404.4114.

Smoothed Gradients for Stochastic Variational Inference

David M. Blei, Stephan Mandt, D. Blei, 2014, NIPS.

The Generalized Reparameterization Gradient

David M. Blei, Francisco J. R. Ruiz, Michalis K. Titsias, 2016, NIPS.

Text-Based Ideal Points pdf

David M. Blei, Keyon Vafa, Suresh Naidu, 2020, ACL.

Dimension Reduction

David M. Blei, Sushant Sachdeva, Xiao Shi, 2019, Computer Vision, A Reference Guide.

Modeling annotated data

Michael I. Jordan, David M. Blei, D. Blei, 2003, SIGIR.

A Nested HDP for Hierarchical Topic Models

Chong Wang, Michael I. Jordan, David M. Blei, 2013, ICLR.

Deep and Hierarchical Implicit Models pdf

Dustin Tran, David M. Blei, Rajesh Ranganath, 2017, ArXiv.

Dynamic topic models

John D. Lafferty, David M. Blei, J. Lafferty, 2006, ICML.

Variational Inference for the Nested Chinese Restaurant Process

Chong Wang, David M. Blei, D. Blei, 2009, NIPS.

Admixtures of latent blocks with application to protein interaction networks

Eric P. Xing, Stephen E. Fienberg, David M. Blei, 2007 .

A Tutorial on Bayesian Nonparametric Models

Samuel J. Gershman, David M. Blei, D. Blei, 2011, 1106.2697.

Linguistic extensions of topic models

David M. Blei, Jordan L. Boyd-Graber, Jordan Boyd-Graber, 2010 .

Sparse stochastic inference for latent Dirichlet allocation

David M. Blei, Matthew D. Hoffman, David M. Mimno, 2012, ICML.

Exponential Family Embeddings

David M. Blei, Francisco J. R. Ruiz, Stephan Mandt, 2016, NIPS.

Zero-Inflated Exponential Family Embeddings

David M. Blei, Li-Ping Liu, 2017, ICML.

The Holdout Randomization Test: Principled and Easy Black Box Feature Selection

Haoran Zhang, David M. Blei, Victor Veitch, 2018, 1811.00645.

Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models

David M. Blei, D. Blei, 2014 .

Visualizing Topic Models

David M. Blei, Allison June-Barlow Chaney, D. Blei, 2012, ICWSM.

Visualizing Topics with Multi-Word Expressions

John D. Lafferty, David M. Blei, J. Lafferty, 2009, 0907.1013.

Correlated Topic Models

John D. Lafferty, David M. Blei, J. Lafferty, 2005, NIPS.

Black Box FDR pdf

David M. Blei, Raul Rabadan, Yixin Wang, 2018, ICML.

Real-time Topic Models for Crisis Counseling

David M. Blei, Karthik Dinakar, Allison J.B. Chaney, 2014 .

Building and using a semantivisual image hierarchy

Fei-Fei Li, David M. Blei, Li-Jia Li, 2010, 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

Scalable Recommendation with Poisson Factorization pdf

David M. Blei, Jake M. Hofman, Prem Gopalan, 2013, ArXiv.

Spatial distance dependent Chinese restaurant processes for image segmentation

Soumya Ghosh, David M. Blei, Erik B. Sudderth, 2011, NIPS.

Content-based recommendations with Poisson factorization

David M. Blei, Laurent Charlin, Prem Gopalan, 2014, NIPS.

Scalable Recommendation with Hierarchical Poisson Factorization

David M. Blei, Jake M. Hofman, Prem Gopalan, 2015, UAI.

Probabilistic topic models

David M. Blei, D. Blei, 2012, Commun. ACM.

A Simple and General Exponential Family Framework for Partial Membership and Factor Analysis

Edoardo M. Airoldi, Stephen E. Fienberg, Katherine A. Heller, 2014 .

Variational Bayesian Inference with Stochastic Search

Michael I. Jordan, David M. Blei, John W. Paisley, 2012, ICML.

Variational Gaussian Process pdf

Dustin Tran, David M. Blei, Rajesh Ranganath, 2015, ICLR.

Panel Discussion

David M. Blei, D. Blei, 2006, SNA@ICML.

Reading Tea Leaves: How Humans Interpret Topic Models

Chong Wang, Sean Gerrish, David M. Blei, 2009, NIPS.

Syntactic Topic Models

David M. Blei, Jordan L. Boyd-Graber, Jordan Boyd-Graber, 2008, NIPS.

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records

George Hripcsak, David M. Blei, Yixin Wang, 2019, MLHC.

Variational inference in nonconjugate models

Chong Wang, David M. Blei, D. Blei, 2012, J. Mach. Learn. Res..

Topic Modeling in Embedding Spaces

David M. Blei, Francisco J. R. Ruiz, Adji B. Dieng, 2019, Transactions of the Association for Computational Linguistics.

Recurrent switching linear dynamical systems

Scott W. Linderman, Ryan P. Adams, Matthew J. Johnson, 2016, 1610.08466.

The Inverse Regression Topic Model

David M. Blei, Maxim Rabinovich, 2014, ICML.

Online Learning for Latent Dirichlet Allocation

Francis R. Bach, David M. Blei, Matthew D. Hoffman, 2010, NIPS.

Learning with Scope, with Application to Information Extraction and Classification

Andrew McCallum, David M. Blei, J. Andrew Bagnell, 2002, UAI.

Copula variational inference

Edoardo M. Airoldi, Dustin Tran, David M. Blei, 2015, NIPS.

Connections between the lines: augmenting social networks with text

David M. Blei, Jordan L. Boyd-Graber, Jonathan Chang, 2009, KDD.

Reweighted Data for Robust Probabilistic Models pdf

David M. Blei, Yixin Wang, Alp Kucukelbir, 2016, ArXiv.

Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference

Michael I. Jordan, David M. Blei, John W. Paisley, 2014, Handbook of Mixed Membership Models and Their Applications.

A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

David M. Blei, Theodore L. Willke, Jeremy R. Manning, 2017 .

The nested Chinese restaurant process and hierarchical topic models

Thomas L. Griffiths, Michael I. Jordan, David M. Blei, 2007 .

Variational Inference for Stick-Breaking Beta Process Priors

Lawrence Carin, David M. Blei, John W. Paisley, 2011, ICML.

Deep Learning with Hierarchical Convolutional Factor Analysis

David B. Dunson, Guillermo Sapiro, Bo Chen, 2013, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Learning with Reflective Likelihoods

Yann LeCun, David M. Blei, Kyunghyun Cho, 2018 .

The nested Chinese restaurant process and Bayesian inference of topic hierarchies

Thomas L. Griffiths, Michael I. Jordan, David M. Blei, 2007 .

A Split-Merge MCMC Algorithm for the Hierarchical Dirichlet Process pdf

Chong Wang, David M. Blei, D. Blei, 2012, ArXiv.

Mixed Membership Stochastic Blockmodels

Edoardo M. Airoldi, Eric P. Xing, Stephen E. Fienberg, 2007, NIPS.

Introduction to Mixed Membership Models and Methods

Edoardo M. Airoldi, Stephen E. Fienberg, David M. Blei, 2014, Handbook of Mixed Membership Models and Their Applications.

Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach.

Scott W. Linderman, Haoran Zhang, David M. Blei, 2021, Biostatistics.

The $χ$-Divergence for Approximate Inference pdf

Dustin Tran, David M. Blei, John W. Paisley, 2016, ArXiv.

Collaborative topic modeling for recommending scientific articles

Chong Wang, David M. Blei, D. Blei, 2011, KDD.

The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies

Thomas L. Griffiths, Michael I. Jordan, David M. Blei, 2007, JACM.

Population Empirical Bayes

David M. Blei, Alp Kucukelbir, D. Blei, 2014, UAI.

Multiple Causes: A Causal Graphical View pdf

Yixin Wang, David M. Blei, D. Blei, 2019, ArXiv.

Proximity Variational Inference

David M. Blei, Rajesh Ranganath, Jaan Altosaar, 2017, AISTATS.

Noise-Based Regularizers for Recurrent Neural Networks

David M. Blei, Rajesh Ranganath, Jaan Altosaar, 2018 .

Modeling User Exposure in Recommendation

David M. Blei, James McInerney, Dawen Liang, 2015, WWW.

Double Empirical Bayes Testing

David Blei, David M. Blei, Yixin Wang, 2020, International statistical review = Revue internationale de statistique.

A Filtering Approach to Stochastic Variational Inference

David M. Blei, Neil Houlsby, 2014, NIPS.

Operator Variational Inference

Dustin Tran, David M. Blei, Rajesh Ranganath, 2016, NIPS.

Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data pdf

David M. Blei, Francisco J. R. Ruiz, Susan Athey, 2018, ArXiv.

Relational Topic Models for Document Networks

David M. Blei, Jonathan Chang, Jonathan D. Chang, 2009, AISTATS.

Variational Inference via \chi Upper Bound Minimization

Dustin Tran, David M. Blei, John W. Paisley, 2016, NIPS.

Towards Clarifying the Theory of the Deconfounder pdf

David M. Blei, Yixin Wang, D. Blei, 2020, ArXiv.

TagLDA: Bringing a document structure knowledge into topic models

Xiaojin Zhu, David M. Blei, John Lafferty, 2006 .

Truncation-free Online Variational Inference for Bayesian Nonparametric Models

Chong Wang, David M. Blei, D. Blei, 2012, NIPS.

A focus on graphical model design and applications to document and image analysis )

David B. Dunson, Lawrence Carin, David M. Blei, 2010 .

Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms

Scott W. Linderman, David M. Blei, C. A. Naesseth, 2016, AISTATS.

Bayesian Checking for Topic Models

David M. Blei, David M. Mimno, D. Blei, 2011, EMNLP.

Overdispersed Black-Box Variational Inference

David M. Blei, Francisco J. R. Ruiz, Michalis K. Titsias, 2016, UAI.

Probabilistic topic models

David B. Dunson, Lawrence Carin, David M. Blei, 2012, Commun. ACM.

Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices pdf

David M. Blei, Christopher Tosh, Wesley Tansey, 2019, ArXiv.

Implicit Causal Models for Genome-wide Association Studies pdf

Dustin Tran, David M. Blei, D. Blei, 2017, ICLR.

Posterior Dispersion Indices pdf

David M. Blei, Alp Kucukelbir, D. Blei, 2016, ArXiv.

Bayesian Nonparametric Poisson Factorization for Recommendation Systems

David M. Blei, Francisco J. R. Ruiz, Rajesh Ranganath, 2014, AISTATS.

Robust Probabilistic Modeling with Bayesian Data Reweighting

David M. Blei, Yixin Wang, Alp Kucukelbir, 2016, ICML.

Using Text Embeddings for Causal Inference pdf

Dhanya Sridhar, David M. Blei, Victor Veitch, 2019, ArXiv.

Deterministic Annealing for Stochastic Variational Inference pdf

Farhan Abrol, David M. Blei, Stephan Mandt, 2014, ArXiv.

Comment: Variational Autoencoders as Empirical Bayes

David M. Blei, Yixin Wang, Andrew C. Miller, 2019, Statistical Science.

NON-PARAMETRIC BAYESIAN ANALYSIS OF HETEROGENEOUS DATA

David M. Blei, D. Blei, 2013 .

Scalable Inference of Overlapping Communities

Michael J. Freedman, Sean Gerrish, David M. Blei, 2012, NIPS.

Hierarchical Implicit Models and Likelihood-Free Variational Inference

Dustin Tran, David M. Blei, Rajesh Ranganath, 2017, NIPS.

A General Method for Robust Bayesian Modeling

Chong Wang, David M. Blei, D. Blei, 2015, Bayesian Analysis.

Profile Predictive Inference pdf

David M. Blei, Alp Kucukelbir, D. Blei, 2014, ArXiv.

Latent Dirichlet Allocation

Michael I. Jordan, Andrew Y. Ng, David M. Blei, 2001, J. Mach. Learn. Res..

Hierarchical bayesian modeling: efficient inference and applications

Chong Wang, David M. Blei, 2012 .

Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence

David M. Blei, Dawen Liang, Jaan Altosaar, 2016, RecSys.

Edward: A library for probabilistic modeling, inference, and criticism pdf

Dustin Tran, David M. Blei, Dawen Liang, 2016, ArXiv.

Variational Sequential Monte Carlo

Scott W. Linderman, David M. Blei, C. A. Naesseth, 2017, AISTATS.

Simultaneous image classification and annotation

Chong Wang, Fei-Fei Li, David M. Blei, 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

Nested Hierarchical Dirichlet Processes

Chong Wang, Michael I. Jordan, David M. Blei, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Word2net: Deep Representations of Language

David M. Blei, Francisco J. R. Ruiz, Maja R. Rudolph, 2018 .

Uncovering, understanding, and predicting links

David M. Blei, Jonathan Chang, Jonathan D. Chang, 2011 .

Structured Embedding Models for Grouped Data

David M. Blei, Francisco J. R. Ruiz, Susan Athey, 2017, NIPS.

Stick-Breaking Beta Processes and the Poisson Process

Michael I. Jordan, David M. Blei, John W. Paisley, 2012, AISTATS.

Nonparametric variational inference pdf

David M. Blei, Matthew D. Hoffman, Samuel Gershman, 2012, ICML.

Statistical Models

David M. Blei, D. Blei, 2014, Encyclopedia of Social Network Analysis and Mining.

Scalable Topic Modeling: Online Learning, Diagnostics, and Recommendation

David M. Blei, D. Blei, 2017 .

Efficient Online Inference for Bayesian Nonparametric Relational Models

David M. Blei, Erik B. Sudderth, Dae Il Kim, 2013, NIPS.

Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation

David M. Blei, Indraneel Mukherjee, D. Blei, 2008, NIPS.

The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling

Chong Wang, David M. Blei, John W. Paisley, 2011, AISTATS.

A probabilistic approach to discovering dynamic full-brain functional connectivity patterns

David M. Blei, Theodore L. Willke, Jeremy R. Manning, 2017, NeuroImage.

Using Embeddings to Correct for Unobserved Confounding pdf

David M. Blei, Victor Veitch, Yixin Wang, 2019, ArXiv.

Decomposing spatiotemporal brain patterns into topographic latent sources

David M. Blei, Per B. Sederberg, Samuel Gershman, 2014, NeuroImage.

Dynamic Bernoulli Embeddings for Language Evolution pdf

David M. Blei, Maja R. Rudolph, D. Blei, 2017, ArXiv.

Variational Bayes under Model Misspecification

Yixin Wang, David M. Blei, D. Blei, 2019, NeurIPS.

A correlated topic model of Science

John D. Lafferty, David M. Blei, J. Lafferty, 2007, 0708.3601.

Simultaneous image classification and annotation

Chong Wang, Fei-Fei Li, David M. Blei, 2009, CVPR.

Extracting information from high-dimensional data: probabilistic modeling, inference and evaluation

David M. Blei, Ingrid Daubechies, Gungor Polatkan, 2012 .

Augment and Reduce: Stochastic Inference for Large Categorical Distributions pdf

David M. Blei, Francisco J. R. Ruiz, Michalis K. Titsias, 2018, ICML.

Variational Tempering

Farhan Abrol, David M. Blei, James McInerney, 2016, AISTATS.

Deep Survival Analysis

Adler J. Perotte, Noémie Elhadad, David M. Blei, 2016, MLHC.

Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data

David M. Blei, Francisco J. R. Ruiz, Tobias S. Schmidt, 2018, ArXiv.

The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019) pdf

David M. Blei, Yixin Wang, 2019, ArXiv.

Prescribed Generative Adversarial Networks pdf

David M. Blei, Francisco J. R. Ruiz, Michalis K. Titsias, 2019, ArXiv.

Topic segmentation with an aspect hidden Markov model

David M. Blei, Pedro J. Moreno, D. Blei, 2001, SIGIR '01.

The Blessings of Multiple Causes

David M. Blei, Yixin Wang, D. Blei, 2018, Journal of the American Statistical Association.

Dynamic Poisson Factorization

David M. Blei, James McInerney, Rajesh Ranganath, 2015, RecSys.

SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements pdf

David M. Blei, Francisco J. R. Ruiz, Susan Athey, 2017, The Annals of Applied Statistics.

A topographic latent source model for fMRI data

David M. Blei, Francisco Pereira, Samuel Gershman, 2011, NeuroImage.

Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts

David M. Blei, Hanna M. Wallach, John W. Paisley, 2015, KDD.

Bayesian Nonparametric Matrix Factorization for Recorded Music

Perry R. Cook, David M. Blei, Matthew D. Hoffman, 2010, ICML.

An Adaptive Learning Rate for Stochastic Variational Inference

Chong Wang, Eric P. Xing, David M. Blei, 2013, ICML.

Adapting Neural Networks for the Estimation of Treatment Effects

David M. Blei, Victor Veitch, Claudia Shi, 2019, NeurIPS.

Matching Words and Pictures

David A. Forsyth, Nando de Freitas, Pinar Duygulu Sahin, 2003, J. Mach. Learn. Res..

Noisin: Unbiased Regularization for Recurrent Neural Networks pdf

David M. Blei, Rajesh Ranganath, Jaan Altosaar, 2018, ICML.

Introduction to Probabilistic Topic Models

David M. Blei, D. Blei, 2010 .

The Issue-Adjusted Ideal Point Model pdf

Sean Gerrish, David M. Blei, D. Blei, 2012, ArXiv.

Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net

David M. Blei, Victoria Kostina, Peter J. Ramadge, 2010, AISTATS.

Nonparametric empirical Bayes for the Dirichlet process mixture model

Michael I. Jordan, David M. Blei, Jon D. McAuliffe, 2006, Stat. Comput..

A Variational Analysis of Stochastic Gradient Algorithms

David M. Blei, Matthew D. Hoffman, Stephan Mandt, 2016, ICML.

Bayesian Nonparametric Models

David M. Blei, 2015 .

Modeling Overlapping Communities with Node Popularities

Chong Wang, David M. Blei, Prem Gopalan, 2013, NIPS.

A latent mixed membership model for relational data

Edoardo M. Airoldi, Eric P. Xing, Stephen E. Fienberg, 2005, LinkKDD '05.

Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data pdf

David M. Blei, Victor Veitch, Peter Orbanz, 2019, AISTATS.

Avoiding Latent Variable Collapse With Generative Skip Models pdf

Alexander M. Rush, David M. Blei, Yoon Kim, 2018, AISTATS.

A Computational Approach to Style in American Poetry

David M. Blei, David M. Kaplan, D. Blei, 2007, Seventh IEEE International Conference on Data Mining (ICDM 2007).

Dirichlet Process Mixtures of Generalized Linear Models

Warren B. Powell, David M. Blei, Lauren Hannah, 2009, J. Mach. Learn. Res..

Dynamic Embeddings for Language Evolution

David M. Blei, Maja R. Rudolph, D. Blei, 2018, WWW.

Black Box Variational Inference

Sean Gerrish, David M. Blei, Rajesh Ranganath, 2013, AISTATS.

Evaluating Bayesian Models with Posterior Dispersion Indices

David M. Blei, Yixin Wang, Alp Kucukelbir, 2017, ICML.

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

Yee Whye Teh, Michael I. Jordan, David M. Blei, 2004, NIPS.

A Bayesian Nonparametric Approach to Image Super-Resolution

Lawrence Carin, David M. Blei, Mingyuan Zhou, 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

Hierarchical Variational Models pdf

Dustin Tran, David M. Blei, Rajesh Ranganath, 2015, ICML.

Technical perspective: Expressive probabilistic models and scalable method of moments

David M. Blei, D. Blei, 2018, Communications of the ACM.

A Topic Model for Word Sense Disambiguation

Xiaojin Zhu, David M. Blei, Jordan L. Boyd-Graber, 2007, EMNLP.

Stochastic Gradient Descent as Approximate Bayesian Inference pdf

David M. Blei, Matthew D. Hoffman, Stephan Mandt, 2017, J. Mach. Learn. Res..

Deep Exponential Families pdf

David M. Blei, Rajesh Ranganath, Linpeng Tang, 2014, AISTATS.

Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve

David M. Blei, Joseph G. Ellis, Maja R. Rudolph, 2015, WWW.

The Deconfounded Recommender: A Causal Inference Approach to Recommendation pdf

David M. Blei, Dawen Liang, Yixin Wang, 2018, ArXiv.

The Population Posterior and Bayesian Modeling on Streams

David M. Blei, James McInerney, Rajesh Ranganath, 2015, NIPS.

Easy As CBA: A Simple Probabilistic Model for Tagging Music

Perry R. Cook, David M. Blei, Matthew D. Hoffman, 2009, ISMIR.

De novo Gene Signature Identification from Single-Cell RNA-Seq with Hierarchical Poisson Factorization

David M. Blei, Francisco J. R. Ruiz, Peter Canoll, 2018, bioRxiv.

The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling

Chong Wang, Katherine A. Heller, David M. Blei, 2010, ICML.

Automatic Differentiation Variational Inference

Dustin Tran, Andrew Gelman, David M. Blei, 2016, J. Mach. Learn. Res..

Variational methods for the Dirichlet process

Michael I. Jordan, David M. Blei, D. Blei, 2004, ICML.

Adapting Text Embeddings for Causal Inference

Dhanya Sridhar, David M. Blei, Victor Veitch, 2020, UAI.

Poisson-Randomized Gamma Dynamical Systems

Scott W. Linderman, David M. Blei, Mingyuan Zhou, 2019, NeurIPS.

Variational inference with copula augmentation pdf

Edoardo M. Airoldi, Dustin Tran, David M. Blei, 2015, ArXiv.

Deep Probabilistic Programming pdf

Dustin Tran, David M. Blei, Matthew D. Hoffman, 2017, ICLR.

Mixed Membership Models

David M. Blei, D. Blei, 2013 .

Hierarchical topographic factor analysis

Jonathan D. Cohen, David M. Blei, Nicholas B. Turk-Browne, 2014, 2014 International Workshop on Pattern Recognition in Neuroimaging.

Variational Inference: A Review for Statisticians

David M. Blei, Jon D. McAuliffe, Alp Kucukelbir, 2016, ArXiv.

Handbook of Mixed Membership Models and Their Applications

Edoardo M. Airoldi, Stephen E. Fienberg, David M. Blei, 2014 .

Content-Based Musical Similarity Computation using the Hierarchical Dirichlet Process

Perry R. Cook, David M. Blei, Matthew D. Hoffman, 2008, ISMIR.

Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans

Scott W. Linderman, David M. Blei, Manuel Zimmer, 2019, bioRxiv.

On the Assumptions of Synthetic Control Methods

Vishal Misra, Dhanya Sridhar, David M. Blei, 2021, AISTATS.

Identifiable Variational Autoencoders via Sparse Decoding pdf

Dhanya Sridhar, David M. Blei, Gemma E. Moran, 2021, ArXiv.

Optimization-based Causal Estimation from Heterogenous Environments pdf

David M. Blei, Yixin Wang, Mingzhang Yin, 2021, ArXiv.

Rationales for Sequential Predictions

Alexander M. Rush, David M. Blei, Yuntian Deng, 2021, EMNLP.

The Holdout Randomization Test for Feature Selection in Black Box Models

David M. Blei, Victor Veitch, Raul Rabadan, 2018, J. Comput. Graph. Stat..

A Proxy Variable View of Shared Confounding

David M. Blei, Yixin Wang, D. Blei, 2021, ICML.

Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections

Dhanya Sridhar, David M. Blei, Victor Veitch, 2021, WWW.

Posterior Predictive Null Checks

David M. Blei, John P. Cunningham, Gemma E. Moran, 2021 .

Conformal Sensitivity Analysis for Individual Treatment Effects

David M. Blei, Claudia Shi, Yixin Wang, 2021, 2112.03493.

A Mixed Membership Approach to the Assessment of Political Ideology from Survey Responses

Edoardo M. Airoldi, Stephen E. Fienberg, David M. Blei, 2014 .

Relational Dose-Response Modeling for Cancer Drug Studies.

David M. Blei, Christopher Tosh, Wesley Tansey, 2019 .

An Analysis of Development of Dementia through the Extended Trajectory Grade of Membership Model

Edoardo M. Airoldi, Stephen E. Fienberg, Fabrizio Lecci, 2014 .

FINDING LATENT SOURCES IN RECORDED MUSIC WITH A SHIFT-INVARIANT HDP

David M. Blei, Matthew D. Hoffman, D. Blei, 2009 .

Correction: A correlated topic model of Science

John D. Lafferty, David M. Blei, J. Lafferty, 2007, 0712.1486.

Poisson Trust Factorization for Incorporating Social Networks into Personalized Item Recommendation

David M. Blei, D. Blei, A. Chaney, 2013 .

Causal Inference for Recommendation

David M. Blei, Dawen Liang, Laurent Charlin, 2016 .

Computational Analysis and Visualized Comparison of Style in American Poetry

David M. Blei, David M. Kaplan, D. Blei, 2006 .

Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span

Michael I. Jordan, David M. Blei, I. Saira Mian, 2006, BMC Bioinformatics.

Dose-response modeling in high-throughput cancer drug screenings: A case study with recommendations for practitioners

Scott W. Linderman, Haoran Zhang, David M. Blei, 2018, 1812.05691.

Discussion of "Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing"

Dustin Tran, David M. Blei, D. Blei, 2016, 1609.05615.

The Blessings of Multiple Causes: Rejoinder

David M. Blei, Yixin Wang, D. Blei, 2019, Journal of the American Statistical Association.

Jordan Boyd-Graber, David Mimno, and David Newman. Care and Feeding of Topic Models: Problems, Diagnostics, and Improvements. Handbook of Mixed Membership Models and Their Applications, 2014.

Edoardo M. Airoldi, Stephen E. Fienberg, Jordan Boyd-Graber, 2014 .