D. Blei

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Bayesian Nonparametrics I

D. Blei, 2016 .

Stochastic Gradient Descent as Approximate Bayesian Inference pdf

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

Hierarchical Bayesian Models for Applications in Information Retrieval

Michael I. Jordan, A. Ng, D. Blei, 2003 .

Latent Dirichlet Allocation

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

Black Box Variational Inference

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

Stochastic variational inference pdf

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

Variational Bayesian Inference with Stochastic Search

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

Context, learning, and extinction.

D. Blei, Y. Niv, S. Gershman, 2010, Psychological review.

Online Learning for Latent Dirichlet Allocation

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

Panel Discussion

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

Admixtures of latent blocks with application to protein interaction networks

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

Collaborative topic modeling for recommending scientific articles

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

Proximity Variational Inference

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

Variational Gaussian Process pdf

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

Matching Words and Pictures

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

A Variational Analysis of Stochastic Gradient Algorithms

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

Nonparametric variational inference pdf

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

Probabilistic topic models

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

Variational Inference: A Review for Statisticians

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

Stochastic Structured Variational Inference

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

Learning with Scope, with Application to Information Extraction and Classification

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

Hierarchical Topic Models and the Nested Chinese Restaurant Process

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

Mixed Membership Stochastic Block Models for Relational Data with Application to Protein-Protein Interactions

E. Xing, S. Fienberg, D. Blei, 2006 .

Stochastic Block Models of Mixed Membership

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

Topic segmentation with an aspect hidden Markov model

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

Modeling annotated data

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

Variational methods for the Dirichlet process

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

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

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

Bayesian Nonparametric Matrix Factorization for Recorded Music

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

Rejection Sampling Variational Inference

Scott W. Linderman, C. A. Naesseth, Francisco J. R. Ruiz, 2016 .

Online Variational Inference for the Hierarchical Dirichlet Process

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

Real-time Topic Models for Crisis Counseling

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

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

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

Hierarchical Implicit Models and Likelihood-Free Variational Inference

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

A correlated topic model of Science

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

Focused Topic Models

D. Blei, K. Heller, Chong Wang, 2009 .

An Adaptive Learning Rate for Stochastic Variational Inference

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

Dynamic topic models

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

Integrating Topics and Syntax

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

A Bayesian Analysis of Dynamics in Free Recall

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

Continuous Time Dynamic Topic Models

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

Variational inference in nonconjugate models

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

Correlated Topic Models

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

Bayesian Inference for Latent Hawkes Processes

Scott W. Linderman, D. Blei, Yixin Wang, 2017 .

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.

Automatic Variational Inference in Stan

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

Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding

D. Blei, Paul DiMaggio, Manish Nag, 2013 .

Reading Tea Leaves: How Humans Interpret Topic Models

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

Operator Variational Inference

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

The nested Chinese restaurant process and Bayesian inference of topic hierarchies

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

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

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

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 Tutorial on Bayesian Nonparametric Models

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

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

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

The nested Chinese restaurant process and hierarchical topic models

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

Nonparametric Bayes Pachinko Allocation

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

Efficient discovery of overlapping communities in massive networks

David M Blei, Prem K Gopalan, D. Blei, 2013, Proceedings of the National Academy of Sciences.

Variational Inference via χ Upper Bound Minimization

Adji B. Dieng, Adji Bousso Dieng, D. Blei, 2017 .

Hierarchical relational models for document networks

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

Deep and Hierarchical Implicit Models pdf

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

Automatic Differentiation Variational Inference

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

Sparse stochastic inference for latent Dirichlet allocation

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

Dirichlet Process Mixtures of Generalized Linear Models

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

Frequentist Consistency of Variational Bayes

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

Variational Inference for Stick-Breaking Beta Process Priors

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

Efficient Online Inference for Bayesian Nonparametric Relational Models

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

Smoothed Gradients for Stochastic Variational Inference

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

Copula variational inference

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

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

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

The Blessings of Multiple Causes

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

Variational Bayes under Model Misspecification

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

Handbook of Mixed Membership Models and Their Applications

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

Poisson-Randomized Gamma Dynamical Systems

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

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records

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

The Issue-Adjusted Ideal Point Model pdf

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

Connections between the lines: augmenting social networks with text

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

Deep Probabilistic Programming pdf

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

Supervised Topic Models

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

Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation

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

Visualizing Topics with Multi-Word Expressions

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

Nonparametric empirical Bayes for the Dirichlet process mixture model

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

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

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

Variational Sequential Monte

Scott W. Linderman, C. A. Naesseth, D. Blei, 2018 .

1 Introduction to Mixed Membership Models and Methods

E. Erosheva, D. Blei, E. Airoldi, 2014 .

De novo gene signature identification from single‐cell RNA‐seq with hierarchical Poisson factorization

Francisco J. R. Ruiz, D. Blei, A. Iavarone, 2018, bioRxiv.

1 Matching Words and Pictures

Michael I. Jordan, N. D. Freitas, D. Blei, 2003 .

Hierarchical Dirichlet Processes Author ( s ) :

Michael I. Jordan, Matthew J. Beal, Y. Teh, 2011 .

Measuring discursive influence across scholarship

Jordan L. Boyd-Graber, James A. Evans, D. Blei, 2018, Proceedings of the National Academy of Sciences.

Spatial distance dependent Chinese restaurant processes for image segmentation

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

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

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

Probabilistic Topic Models

Lawrence Carin, David Blei, David Dunson, 2010, IEEE Signal Processing Magazine.

Overlapping clustering methods for networks

E. Erosheva, D. Blei, P. Latouche, 2019 .

Adjusting for indirectly measured confounding using large-scale propensity score

D. Blei, M. Schuemie, G. Hripcsak, 2021, J. Biomed. Informatics.

Interpreting Mixed Membership Models : Implications of Erosheva ’ s Representation Theorem

S. Fienberg, E. Erosheva, D. Blei, 2018 .

D. Blei, R. Ranganath, 2018 .

Poisson Trust Factorization for Incorporating Social Networks into Personalized Item Recommendation

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

Factor Topographic Latent Source Analysis : Factor Analysis for Brain Images ?

Jeremy R. Manning, D. Blei, S. Gershman, 2012 .

The Ideal Point Topic Model : Predicting Legislative Roll Calls from Text

D. Blei, S. Gerrish, 2010 .

Hierarchical Dirichlet Processes

Michael I. Jordan, Matthew J. Beal, Y. Teh, 2006 .

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 .

Nonparametric Mixed Membership Modelling Using the IBP Compound Dirichlet Process

D. Blei, K. Heller, Chong Wang, 2011 .

Markovian Score Climbing: Variational Inference with KL(p||q)

C. A. Naesseth, D. Blei, F. Lindsten, 2020, NeurIPS.

A Bayesian Boosting Model

R. Schapire, P. Ramadge, D. Blei, 2012, 1209.1996.

Learning with Reflective Likelihoods

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

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.

Conformal Sensitivity Analysis for Individual Treatment Effects

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

Proceedings of the 2006 conference on Statistical network analysis

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

Technical perspective: Expressive probabilistic models and scalable method of moments

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

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

David M. Blei, D. Blei, 2012 .

A Proxy Variable View of Shared Confounding

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

The Blessings of Multiple Causes: Rejoinder

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

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.

Mixed Membership Models

David M. Blei, D. Blei, 2013 .

Comment

D. Blei, 2016 .

Comment

D. Blei, Dustin Tran, 2017 .

A probabilistic approach to full-brain functional connectivity analyses

Jeremy R. Manning, D. Blei, R. Ranganath, 2014 .

Black Box FDR pdf

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

Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure

D. Blei, B. Engelhardt, D. Mimno, 2014, Proceedings of the National Academy of Sciences.

The Posterior Predictive Null

Gemma E. Moran, J. Cunningham, D. Blei, 2021, Bayesian Analysis.

Heterogeneous Supervised Topic Models

D. Blei, Hal Daumé, Dhanya Sridhar, 2022, Transactions of the Association for Computational Linguistics.

The Posterior Predictive Null

Gemma E. Moran, J. Cunningham, D. Blei, 2021, Bayesian Analysis.

Conformal Sensitivity Analysis for Individual Treatment Effects

D. Blei, Mingzhang Yin, Yixin Wang, 2021, Journal of the American Statistical Association.

Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse

D. Blei, Carl Vondrick, Sachit Menon, 2022, UAI.

Appendix to Variational Inference: A Review for Statisticians

Jon D. McAuliffe, D. Blei, A. Kucukelbir, 2017 .

Probabilistic Conformal Prediction Using Conditional Random Samples

D. Blei, Mingyuan Zhou, Mingzhang Yin, 2022, AISTATS.

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

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

Variational Inference for Infinitely Deep Neural Networks

D. Blei, Achille Nazaret, 2022, ICML.

Computational Analysis and Visualized Comparison of Style in American Poetry

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

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

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

Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport

C. A. Naesseth, D. Blei, Liyi Zhang, 2022, Trans. Mach. Learn. Res..

The $\chi$-Divergence for Approximate Inference

Adji Bousso Dieng, D. Blei, R. Ranganath, 2016, 1611.00328.

Posterior Collapse and Latent Variable Non-identifiability

J. Cunningham, D. Blei, Yixin Wang, 2023, NeurIPS.

Reconstructing the Universe with Variational self-Boosted Sampling

D. Blei, Yin Li, C. Modi, 2022, 2206.15433.

The Population Posterior and Bayesian Inference on Streams

D. Blei, R. Ranganath, James McInerney, 2015, 1507.05253.

Measuring discursive inﬂuence across scholarship

Jordan L. Boyd-Graber, James A. Evans, D. Blei, 2018 .

Causal inference from text: A commentary

D. Blei, Dhanya Sridhar, 2022, Science advances.

CAREER: Transfer Learning for Economic Prediction of Labor Sequence Data

D. Blei, S. Athey, Keyon Vafa, 2022, 2202.08370.

Learning Transferrable Representations of Career Trajectories for Economic Prediction

D. Blei, S. Athey, Keyon Vafa, 2022, ArXiv.

The Discrete Innite Logistic Normal Distribution

D. Blei, Chong Wang, J. Paisley, 2011, 1103.4789.

A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making

D. Blei, G. Hripcsak, Yixin Wang, 2022, ArXiv.

Counterfactual inference for consumer choice across many product categories

D. Blei, S. Athey, Rob Donnelly, 2021, Quantitative Marketing and Economics.

Starfysh reveals heterogeneous spatial dynamics in the breast tumor microenvironment

D. Blei, E. Azizi, G. Plitas, 2022, bioRxiv.

Probabilistic topic models

David B. Dunson, Lawrence Carin, David M. Blei, 2011, KDD '11 Tutorials.

A Bayesian model of dose-response for cancer drug studies

D. Blei, Wesley Tansey, Christopher Tosh, 2022, The Annals of Applied Statistics.

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

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

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

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

Model-based Classification

D. Blei, 2019, Model-Based Clustering and Classification for Data Science.

Text-Based Ideal Points pdf

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

Applications of latent variable models in modeling influence and decision making

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

How They Vote: Issue-Adjusted Models of Legislative Behavior

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

Predicting Legislative Roll Calls from Text

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

A General Method for Robust Bayesian Modeling

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

Population Empirical Bayes

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

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

David M. Blei, D. Blei, 2014 .

Counterfactual inference for consumer choice across many product categories pdf

Susan Athey, Rob Donnelly, Francisco R. Ruiz, 2019, Quantitative Marketing and Economics.

Double Empirical Bayes Testing

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

Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net

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

Augment and Reduce: Stochastic Inference for Large Categorical Distributions pdf

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

Exponential Family Embeddings

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

Mixed Membership Stochastic Blockmodels

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

Relational Dose-Response Modeling for Cancer Drug Studies.

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

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

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

Correction: A correlated topic model of Science

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

Modeling Overlapping Communities with Node Popularities

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

Scalable Inference of Overlapping Communities

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

Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference

David Blei, C. A. Naesseth, Christian A. Naesseth, 2021, UAI.

Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations

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

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

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

Scalable Recommendation with Poisson Factorization pdf

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

Second primary malignancy after Hodgkin's disease, ovarian cancer and cancer of the testis: a population-based cohort study.

DM Blei, K Franks, MI Jordan, 1987, British Journal of Cancer.

The Markov link method: a nonparametric approach to combine observations from multiple experiments

David Blei, Hongkui Zeng, Liam Paninski, 2018, bioRxiv.

Identifiable Deep Generative Models via Sparse Decoding

Gemma E. Moran, D. Blei, Yixin Wang, 2021, Trans. Mach. Learn. Res..

Hierarchical Variational Models pdf

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

Adapting Neural Networks for the Estimation of Treatment Effects

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

Distance dependent Chinese restaurant processes

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

Adjusting for Unobserved Confounding Using Large-Scale Propensity Scores

George Hripcsak, David Blei, Linying Zhang, 2021 .

Probabilistic models of text and images

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

Stochastic Search with an Observable State Variable

Warrren B Powell, D. Blei, Lauren Hannah, 2010, 1006.4338.

Deep Survival Analysis

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

Implicit Causal Models for Genome-wide Association Studies pdf

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

Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data

Jeremy R. Manning, D. Blei, R. Ranganath, 2014, PLoS ONE.

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

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

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

Scott W. Linderman, Haoran Zhang, Chris H Wiggins, 2018, Biostatistics.

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.

Structured Stochastic Variational Inference

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

Relational Topic Models for Document Networks

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

Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable

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

Avoiding Latent Variable Collapse With Generative Skip Models pdf

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

A Probabilistic Model of Cardiac Physiology and Electrocardiograms pdf

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

Syntactic Topic Models

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

A Topic Model for Word Sense Disambiguation

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

The Holdout Randomization Test for Feature Selection in Black Box Models

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

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

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

Introduction to Probabilistic Topic Models

David M. Blei, D. Blei, 2010 .

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

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

Linguistic extensions of topic models

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

Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process

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

Multilingual Topic Models for Unaligned Text pdf

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

Markov Topic Models

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

A Language-based Approach to Measuring Scholarly Impact

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

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.

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.

NON-PARAMETRIC BAYESIAN ANALYSIS OF HETEROGENEOUS DATA

David M. Blei, D. Blei, 2013 .

Dynamic and Supervised Topic Models for Literature-Based Discovery

David M. Blei, D. Blei, 2011 .

Prescribed Generative Adversarial Networks pdf

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

Hierarchical maximum entropy density estimation

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

Topic Modeling in Embedding Spaces

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

Distance Dependent Infinite Latent Feature Models

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

On the Misspecification of Linear Assumptions in Synthetic Control

D. Blei, Achille Nazaret, Claudia Shi, 2023, 2302.12777.

Truncation-free stochastic variational inference for Bayesian nonparametric models

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

Deep Learning with Hierarchical Convolutional Factor Analysis

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

Towards Clarifying the Theory of the Deconfounder pdf

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

Optimization-based Causal Estimation from Heterogenous Environments

D. Blei, Mingzhang Yin, Yixin Wang, 2021, J. Mach. Learn. Res..

Invariant Representation Learning for Treatment Effect Estimation

David Blei, Victor Veitch, Claudia Shi, 2020, UAI.

Overdispersed Black-Box Variational Inference

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

Index of authors, volume 170, 2007

D. Hunter, S. Fienberg, J. Besag, 2007 .

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

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

Hierarchical topographic factor analysis

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

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).

Probabilistic topic models

David Blei, D. Blei, 2011, KDD '11 Tutorials.

A Probabilistic Model for Using Social Networks in Personalized Item Recommendation

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

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

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

Using Embeddings to Correct for Unobserved Confounding

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

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.

Stick-Breaking Beta Processes and the Poisson Process

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

Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems

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

A Bayesian Nonparametric Approach to Image Super-Resolution

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

Causal Inference for Recommender Systems

D. Blei, Laurent Charlin, Yixin Wang, 2020, RecSys.

Causal Inference for Recommendation

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

Scalable Recommendation with Hierarchical Poisson Factorization

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

Adapting Text Embeddings for Causal Inference

D. Blei, Dhanya Sridhar, Victor Veitch, 2019, UAI.

Identifiable Variational Autoencoders via Sparse Decoding pdf

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

Modeling User Exposure in Recommendation

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

The Generalized Reparameterization Gradient

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

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

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

Nonparametric Identifiability of Causal Representations from Unknown Interventions

Julius von Kügelgen, E. Bareinboim, B. Schölkopf, 2023, ArXiv.

Visualizing Topic Models

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

Variational Inference via \chi Upper Bound Minimization

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

Continuous-Time Limit of Stochastic Gradient Descent Revisited

D. Blei, S. Mandt, 2015 .

Scaling probabilistic models of genetic variation to millions of humans

John D. Storey, D. Blei, Prem Gopalan, 2014, Nature Genetics.

Bayesian Nonparametric Poisson Factorization for Recommendation Systems

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

The Dynamic Embedded Topic Model pdf

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

Variational Sequential Monte Carlo

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

Science and data science

Padhraic Smyth, David M Blei, Padhraic Smyth, 2017, Proceedings of the National Academy of Sciences.

Equation Embeddings pdf

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

Variational Inference for Adaptor Grammars

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

Robust Probabilistic Modeling with Bayesian Data Reweighting

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

Content-based recommendations with Poisson factorization

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

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.

Recurrent switching linear dynamical systems

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

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..

Mapping interstellar dust with Gaussian processes

Andrew C. Miller, J. Cunningham, D. Blei, 2022, Annals of Applied Statistics.

On the Assumptions of Synthetic Control Methods

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

The Deconfounded Recommender: A Causal Inference Approach to Recommendation pdf

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

Interpretability Constraints and Trade-offs in Using Mixed Membership Models

S. Fienberg, E. Erosheva, Zoubin Ghahramani, 2014 .

Practical and Asymptotically Exact Conditional Sampling in Diffusion Models

C. A. Naesseth, Brian L. Trippe, D. Blei, 2023, NeurIPS.

Dynamic Embeddings for Language Evolution

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

Dynamic Bernoulli Embeddings for Language Evolution pdf

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

General linear-time inference for Gaussian Processes on one dimension

J. Cunningham, L. Paninski, D. Blei, 2020, ArXiv.

An Invariant Learning Characterization of Controlled Text Generation

D. Blei, Amir Feder, Keyon Vafa, 2023, ACL.

Unsupervised Representation Learning via Neural Activation Coding

David Blei, Gunhee Kim, Sangho Lee, 2021, ICML.

Readmission prediction via deep contextual embedding of clinical concepts

Fei Wang, Cao Xiao, Tengfei Ma, 2018, PloS one.

Noisin: Unbiased Regularization for Recurrent Neural Networks pdf

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

Evaluating the Moral Beliefs Encoded in LLMs

D. Blei, Amir Feder, Claudia Shi, 2023, NeurIPS.

Bayesian Checking for Topic Models

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

Hierarchical Inducing Point Gaussian Process for Inter-domain Observations

David Blei, Luhuan Wu, John Cunningham, 2021, AISTATS.

Using Text Embeddings for Causal Inference pdf

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

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.

Population Predictive Checks pdf

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

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

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

Structured Embedding Models for Grouped Data

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

Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2020 U.S. Presidential Election

D. Blei, D. Green, Aaron Schein, 2021 .

Density Uncertainty Layers for Reliable Uncertainty Estimation

D. Blei, Yookoon Park, 2023, AISTATS.

Dynamic topic models

J. Lafferty, D. Blei, 2006, ICML.

The $χ$-Divergence for Approximate Inference pdf

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

Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms

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

Rationales for Sequential Predictions

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

Comment: Variational Autoencoders as Empirical Bayes

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

Truncation-free Online Variational Inference for Bayesian Nonparametric Models

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

Posterior Collapse and Latent Variable Non-identifiability

J. Cunningham, D. Blei, Yixin Wang, 2023, NeurIPS.

Deep Exponential Families pdf

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

use a Gaussian random walk to capture drift in the underlying language model ; for example

Maja R. Rudolph, D. Blei, 2018 .

Modeling Influence in Text Corpora

David Blei, Sean Gerrish, D. Blei, 2009 .

Easy As CBA: A Simple Probabilistic Model for Tagging Music

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

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

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

Statistical discovery of signaling pathways from an ensemble of weakly informative data sources

F. Markowetz, D. Blei, O. Troyanskaya, 2007 .

Statistical Models

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

Content-Based Musical Similarity Computation using the Hierarchical Dirichlet Process

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

Evaluating Bayesian Models with Posterior Dispersion Indices

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

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

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

TagLDA: Bringing a document structure knowledge into topic models

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

A topographic latent source model for fMRI data

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

Hierarchical relational models for document networks

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

Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis

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

Reading Tea Leaves: How Humans Interpret Topic Models

Jordan L. Boyd-Graber, Jonathan D. Chang, D. Blei, 2009, NIPS.

Estimating Social Influence from Observational Data

D. Blei, Dhanya Sridhar, C. D. Bacco, 2022, CLeaR.

v 3 COMS E 6998-002 : Probabilistic Modeling for Discrete Data Lecture 3 : Word Embeddings III Instructor

D. Blei, 2022 .

Amortized Variational Inference: When and Why?

D. Blei, C. Margossian, 2023, ArXiv.

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).

Foundations of Graphical Models

D. Blei, 2014 .

Title Serendipity based recommender system for perovskites material discovery: balancing exploration and exploitation across multiple models [short] Serendipity recommender for perovskites discovery

Sorelle A. Friedler, Gemma E. Moran, D. Blei, 2022 .

Multiple Causes: A Causal Graphical View

D. Blei, Yixin Wang, 2019, ArXiv.

The Blessings of Multiple Causes

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

Deterministic Annealing for Stochastic Variational Inference pdf

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

Variational Inference for the Nested Chinese Restaurant Process

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

Posterior Predictive Null Checks

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

Latent Dirichlet Allocation

Michael I. Jordan, A. Ng, D. Blei, 2009 .

Equal Opportunity and Affirmative Action via Counterfactual Predictions pdf

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

Equal Opportunity and Afﬁrmative Action via Counterfactual Predictions

D. Blei, Yixin Wang, Dhanya Sridhar, 2022 .

Reading Tea Leaves: How Humans Interpret Topic Models

Jordan L. Boyd-Graber, Jonathan D. Chang, D. Blei, 2009, NIPS.

Probabilistic topic models

D. Blei, 2011, KDD '11 Tutorials.

Nonparametric Identifiability of Causal Representations from Unknown Interventions

Julius von Kügelgen, E. Bareinboim, D. Blei, 2023, NeurIPS.

Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes

Michael I. Jordan, Matthew J. Beal, Y. Teh, 2004, NIPS.

Science and data science

Padhraic Smyth, D. Blei, 2017, Proceedings of the National Academy of Sciences.

Statistical Network Analysis: Models, Issues, and New Directions - ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, Revised Selected Papers

S. Fienberg, D. Blei, E. Airoldi, 2007, SNA@ICML.

Scalable Topic Modeling: Online Learning, Diagnostics, and Recommendation

David M. Blei, D. Blei, 2017 .

Decomposing spatiotemporal brain patterns into topographic latent sources

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

Generative Models for Decoding Real-Valued Natural Experience in FMRI

D. Blei, G. Stephens, 2006 .

A Topographic Latent Source Model for Fmri Data

D. Blei, Samuel J. Gershman, K. A. Norman, 2022 .

The Variational Gaussian Process

D. Blei, R. Ranganath, Dustin Tran, 2015, ICLR 2015.

Multiple Causes: A Causal Graphical View pdf

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

Implicit Causal Models for Genome-wide Association Studies

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

Latent Dirichlet Allocation

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

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

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

Detecting and Characterizing Events

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

Matrix Factorization

Michael I. Jordan, A. Ng, D. Blei, 2018, Encyclopedia of Social Network Analysis and Mining. 2nd Ed..

Scaling probabilistic models of genetic variation to millions of humans

John D. Storey, D. Blei, Prem Gopalan, 2014, Nature Genetics.

Posterior Dispersion Indices pdf

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

Profile Predictive Inference pdf

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

Correction to: Counterfactual inference for consumer choice across many product categories

D. Blei, S. Athey, Rob Donnelly, 2021, Quantitative Marketing and Economics.

De novo gene signature identification from single‐cell RNA‐seq with hierarchical Poisson factorization

Francisco J. R. Ruiz, D. Blei, A. Iavarone, 2018, bioRxiv.

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.

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

Michael I. Jordan, T. Griffiths, D. Blei, 2007, JACM.

Hierarchical relational models for document networks

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

A Nested HDP for Hierarchical Topic Models

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

Distance dependent Chinese restaurant processes

D. Blei, Peter I. Frazier, 2009, ICML.

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

D. Blei, 2012 .

Uncovering, understanding, and predicting links

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

Syntactic Topic Models

Jordan L. Boyd-Graber, D. Blei, 2008, NIPS.

A Topic Model for Word Sense Disambiguation

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

Variational inference with copula augmentation pdf

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

Causal fairness assessment of treatment allocation with electronic health records

D. Blei, G. Hripcsak, Yixin Wang, 2022, J. Biomed. Informatics.

Revisiting Topic-Guided Language Models

D. Blei, Keyon Vafa, Carolina Zheng, 2023, Trans. Mach. Learn. Res..

Matching Words and Pictures

Nando de Freitas, Michael I. Jordan, D. Blei, 2003, J. Mach. Learn. Res..