David Blei

Q: What Schools Are Affiliated With David Blei

David Blei is affiliated with the following schools: Brown University, University of California, Berkeley, Princeton University, Columbia University

David Blei's AcademicInfluence.com Rankings

David Blei

Computer Science

#317

World Rank

#330

Historical Rank

#178

USA Rank

Machine Learning

#27

World Rank

#27

Historical Rank

#11

USA Rank

Artificial Intelligence

#128

World Rank

#131

Historical Rank

#59

USA Rank

Database

#174

World Rank

#180

Historical Rank

#87

USA Rank

computer-science Degrees

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Computer Science

David Blei's Degrees

PhD Computer Science University of California, Berkeley
Bachelors Computer Science Brown University

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Why Is David Blei Influential?

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According to Wikipedia, David Meir Blei is a professor in the Statistics and Computer Science departments at Columbia University. Prior to fall 2014 he was an associate professor in the Department of Computer Science at Princeton University. His work is primarily in machine learning.

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David Blei's Published Works

Number of citations in a given year to any of this author's works

Total number of citations to an author for the works they published in a given year. This highlights publication of the most important work(s) by the author

Published Works

Latent Dirichlet Allocation (2001) (34032)
Probabilistic Topic Models (2010) (5293)
Hierarchical Dirichlet Processes (2006) (3732)
Variational Inference: A Review for Statisticians (2016) (3283)
Dynamic topic models (2006) (2465)
Stochastic variational inference (2012) (2239)
Mixed Membership Stochastic Blockmodels (2007) (2014)
Reading Tea Leaves: How Humans Interpret Topic Models (2009) (1987)
Matching Words and Pictures (2003) (1797)
Supervised Topic Models (2007) (1714)
Online Learning for Latent Dirichlet Allocation (2010) (1622)
Collaborative topic modeling for recommending scientific articles (2011) (1590)
A correlated topic model of Science (2007) (1344)
Modeling annotated data (2003) (1248)
Hierarchical Topic Models and the Nested Chinese Restaurant Process (2003) (1120)
Correlated Topic Models (2005) (1101)
Black Box Variational Inference (2013) (944)
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies (2007) (667)
Integrating Topics and Syntax (2004) (615)
Exploiting affinities between topic modeling and the sociological perspective on culture: Application to newspaper coverage of U.S. government arts funding (2013) (613)
Simultaneous image classification and annotation (2009) (610)
Automatic Differentiation Variational Inference (2016) (569)
Relational Topic Models for Document Networks (2009) (563)
A Tutorial on Bayesian Nonparametric Models (2011) (532)
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes (2004) (487)
Stochastic Gradient Descent as Approximate Bayesian Inference (2017) (478)
Continuous Time Dynamic Topic Models (2008) (476)
Variational Bayesian Inference with Stochastic Search (2012) (433)
Bayesian Nonparametrics I (2016) (401)
Online Variational Inference for the Hierarchical Dirichlet Process (2011) (389)
Distance dependent Chinese restaurant processes (2009) (379)
Context, learning, and extinction. (2010) (301)
Modeling User Exposure in Recommendation (2015) (293)
Hierarchical Variational Models (2015) (289)
Edward: A library for probabilistic modeling, inference, and criticism (2016) (288)
Efficient discovery of overlapping communities in massive networks (2013) (288)
Build, Compute, Critique, Repeat: Data Analysis with Latent Variable Models (2014) (279)
A Topic Model for Word Sense Disambiguation (2007) (267)
Topic Modeling in Embedding Spaces (2019) (264)
Hierarchical relational models for document networks (2009) (252)
Introduction to Probabilistic Topic Models (2010) (244)
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence (2016) (241)
Scalable Recommendation with Hierarchical Poisson Factorization (2015) (229)
Visualizing Topic Models (2012) (225)
Hierarchical Implicit Models and Likelihood-Free Variational Inference (2017) (219)
Syntactic Topic Models (2008) (212)
Variational inference in nonconjugate models (2012) (211)
Automatic Variational Inference in Stan (2015) (208)
Topic segmentation with an aspect hidden Markov model (2001) (202)
The Blessings of Multiple Causes (2018) (200)
Nested Hierarchical Dirichlet Processes (2012) (192)
Dirichlet Process Mixtures of Generalized Linear Models (2009) (190)
Variational methods for the Dirichlet process (2004) (186)
Deep Probabilistic Programming (2017) (182)
Multilingual Topic Models for Unaligned Text (2009) (178)
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation (2015) (177)
Predicting Legislative Roll Calls from Text (2011) (175)
Content-based recommendations with Poisson factorization (2014) (173)
Variational Sequential Monte Carlo (2017) (173)
Adapting Neural Networks for the Estimation of Treatment Effects (2019) (171)
The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling (2010) (167)
Variational Gaussian Process (2015) (166)
Bayesian Nonparametric Matrix Factorization for Recorded Music (2010) (164)
Panel Discussion (2006) (161)
Deep Survival Analysis (2016) (159)
A Language-based Approach to Measuring Scholarly Impact (2010) (157)
Frequentist Consistency of Variational Bayes (2017) (153)
Sparse stochastic inference for latent Dirichlet allocation (2012) (153)
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems (2017) (153)
Scalable Recommendation with Poisson Factorization (2013) (151)
The Generalized Reparameterization Gradient (2016) (149)
Connections between the lines: augmenting social networks with text (2009) (147)
Deep Exponential Families (2014) (146)
Avoiding Latent Variable Collapse With Generative Skip Models (2018) (143)
Mixed Membership Stochastic Block Models for Relational Data with Application to Protein-Protein Interactions (2006) (140)
Nonparametric variational inference (2012) (139)
Nonparametric empirical Bayes for the Dirichlet process mixture model (2006) (136)
Building and using a semantivisual image hierarchy (2010) (132)
Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process (2009) (131)
Dynamic Embeddings for Language Evolution (2018) (123)
A Variational Analysis of Stochastic Gradient Algorithms (2016) (120)
Science and data science (2017) (118)
Operator Variational Inference (2016) (109)
Scalable Inference of Overlapping Communities (2012) (109)
Deep Learning with Hierarchical Convolutional Factor Analysis (2013) (106)
Variational Inference via χ Upper Bound Minimization (2017) (103)
Easy As CBA: A Simple Probabilistic Model for Tagging Music (2009) (103)
Visualizing Topics with Multi-Word Expressions (2009) (102)
Exponential Family Embeddings (2016) (101)
How They Vote: Issue-Adjusted Models of Legislative Behavior (2012) (100)
Readmission prediction via deep contextual embedding of clinical concepts (2018) (99)
Bayesian Checking for Topic Models (2011) (98)
Bayesian Nonparametric Poisson Factorization for Recommendation Systems (2014) (97)
An Adaptive Learning Rate for Stochastic Variational Inference (2013) (93)
Deep and Hierarchical Implicit Models (2017) (92)
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms (2016) (92)
Nonparametric Bayes Pachinko Allocation (2007) (90)
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts (2015) (89)
Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis (2015) (89)
A Bayesian Nonparametric Approach to Image Super-Resolution (2012) (88)
Dynamic Poisson Factorization (2015) (88)
Probabilistic models of text and images (2004) (82)
Copula variational inference (2015) (80)
Spatial distance dependent Chinese restaurant processes for image segmentation (2011) (78)
Causal Inference for Recommendation (2016) (76)
Structured Stochastic Variational Inference (2014) (76)
Content-Based Musical Similarity Computation using the Hierarchical Dirichlet Process (2008) (75)
Variational Inference via \chi Upper Bound Minimization (2016) (74)
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations (2016) (70)
Causal Inference for Recommender Systems (2020) (68)
Stochastic Structured Variational Inference (2014) (67)
Variational Inference for the Nested Chinese Restaurant Process (2009) (67)
Truncation-free Online Variational Inference for Bayesian Nonparametric Models (2012) (66)
De novo gene signature identification from single‐cell RNA‐seq with hierarchical Poisson factorization (2018) (66)
Robust Probabilistic Modeling with Bayesian Data Reweighting (2016) (65)
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements (2017) (64)
Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference (2014) (63)
Hierarchical recurrent state space models reveal discrete and continuous dynamics of neural activity in C. elegans (2019) (62)
Markov Topic Models (2009) (60)
The Deconfounded Recommender: A Causal Inference Approach to Recommendation (2018) (60)
A latent mixed membership model for relational data (2005) (58)
Stochastic Block Models of Mixed Membership (2006) (57)
Prescribed Generative Adversarial Networks (2019) (56)
Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data (2018) (54)
The Dynamic Embedded Topic Model (2019) (53)
The Inverse Regression Topic Model (2014) (51)
Hierarchical Bayesian Models for Applications in Information Retrieval (2003) (50)
Surveying a suite of algorithms that offer a solution to managing large document archives. (2012) (50)
A Computational Approach to Style in American Poetry (2007) (50)
Stick-Breaking Beta Processes and the Poisson Process (2012) (50)
Variational Tempering (2014) (49)
The Discrete Innite Logistic Normal Distribution (2011) (49)
A Split-Merge MCMC Algorithm for the Hierarchical Dirichlet Process (2012) (48)
Adapting Text Embeddings for Causal Inference (2019) (47)
Handbook of Mixed Membership Models and Their Applications (2014) (46)
A General Method for Robust Bayesian Modeling (2015) (45)
Probabilistic Topic Models: A focus on graphical model design and applications to document and image analysis. (2010) (43)
Recurrent switching linear dynamical systems (2016) (43)
Scaling probabilistic models of genetic variation to millions of humans (2014) (43)
Topographic Factor Analysis: A Bayesian Model for Inferring Brain Networks from Neural Data (2014) (42)
The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling (2011) (42)
Variational Inference for Stick-Breaking Beta Process Priors (2011) (40)
Overdispersed Black-Box Variational Inference (2016) (40)
Truncation-free stochastic variational inference for Bayesian nonparametric models (2012) (39)
Learning with Scope, with Application to Information Extraction and Classification (2002) (39)
Implicit Causal Models for Genome-wide Association Studies (2017) (39)
Variational Inference for Adaptor Grammars (2010) (38)
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net (2010) (37)
Measuring discursive influence across scholarship (2018) (37)
Using Embeddings to Correct for Unobserved Confounding in Networks (2019) (35)
Efficient Online Inference for Bayesian Nonparametric Relational Models (2013) (34)
Structured Embedding Models for Grouped Data (2017) (34)
The Population Posterior and Bayesian Modeling on Streams (2015) (33)
Distance Dependent Infinite Latent Feature Models (2011) (33)
Dynamic Bernoulli Embeddings for Language Evolution (2017) (33)
A topographic latent source model for fMRI data (2011) (32)
The Holdout Randomization Test: Principled and Easy Black Box Feature Selection (2018) (31)
A Bayesian Analysis of Dynamics in Free Recall (2009) (31)
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable (2010) (31)
Smoothed Gradients for Stochastic Variational Inference (2014) (30)
Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span (2006) (30)
Introduction to Mixed Membership Models and Methods (2014) (30)
Variational Bayes under Model Misspecification (2019) (29)
Correction: A correlated topic model of Science (2007) (28)
PUTOP: Turning Predominant Senses into a Topic Model for Word Sense Disambiguation (2007) (27)
TagLDA: Bringing a document structure knowledge into topic models (2006) (27)
Markovian Score Climbing: Variational Inference with KL(p||q) (2020) (26)
The Survival Filter: Joint Survival Analysis with a Latent Time Series (2015) (25)
Hierarchical maximum entropy density estimation (2007) (25)
Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation (2008) (25)
Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis (2006) (24)
Probabilistic topic models (2011) (24)
Posterior Collapse and Latent Variable Non-identifiability (2023) (23)
A probabilistic approach to discovering dynamic full-brain functional connectivity patterns (2017) (22)
FINDING LATENT SOURCES IN RECORDED MUSIC WITH A SHIFT-INVARIANT HDP (2009) (22)
Augment and Reduce: Stochastic Inference for Large Categorical Distributions (2018) (21)
Correlated Random Measures (2015) (21)
Posterior predictive checks to quantify lack-of-fit in admixture models of latent population structure (2014) (21)
Correction to: Counterfactual inference for consumer choice across many product categories (2019) (20)
Data-Driven Recomposition using the Hierarchical Dirichlet Process Hidden Markov Model (2009) (19)
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve (2015) (19)
Modeling Overlapping Communities with Node Popularities (2013) (19)
Equation Embeddings (2018) (19)
The Holdout Randomization Test for Feature Selection in Black Box Models (2018) (18)
The nested Chinese restaurant process and Bayesian inference of topic hierarchies (2007) (18)
Noisin: Unbiased Regularization for Recurrent Neural Networks (2018) (18)
Using Text Embeddings for Causal Inference (2019) (18)
Equal Opportunity and Affirmative Action via Counterfactual Predictions (2019) (15)
Invariant Representation Learning for Treatment Effect Estimation (2020) (15)
Bayesian Inference for Latent Hawkes Processes (2017) (15)
Towards Clarifying the Theory of the Deconfounder (2020) (15)
Continuous-Time Limit of Stochastic Gradient Descent Revisited (2015) (15)
Poisson-Randomized Gamma Dynamical Systems (2019) (14)
Deep learning? (1999) (14)
Detecting and Characterizing Events (2016) (14)
Decomposing spatiotemporal brain patterns into topographic latent sources (2014) (13)
Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span (2006) (13)
A Mixed Membership Approach to the Assessment of Political Ideology from Survey Responses (2014) (13)
The nested Chinese restaurant process and hierarchical topic models (2007) (13)
Identifiable Variational Autoencoders via Sparse Decoding (2021) (13)
Proximity Variational Inference (2017) (13)
Bayesian Spectral Matching: Turning Young MC into MC Hammer via MCMC Sampling (2009) (13)
Population Empirical Bayes (2014) (13)
Real-time Topic Models for Crisis Counseling (2014) (13)
Using Embeddings to Correct for Unobserved Confounding (2019) (12)
General linear-time inference for Gaussian Processes on one dimension (2020) (12)
Focused Topic Models (2009) (12)
Context Selection for Embedding Models (2017) (12)
Reweighted Data for Robust Probabilistic Models (2016) (12)
1 Matching Words and Pictures (2003) (12)
Black Box FDR (2018) (12)
Conformal Sensitivity Analysis for Individual Treatment Effects (2021) (11)
Rationales for Sequential Predictions (2021) (11)
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. (2014) (10)
Mixed membership analysis of high-throughput interaction studies: Relational data (2007) (10)
Multiple Causes: A Causal Graphical View (2019) (10)
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records (2019) (10)
Hierarchical topographic factor analysis (2014) (10)
Hierarchical Regression (2014) (9)
Applications of latent variable models in modeling influence and decision making (2013) (9)
The $χ$-Divergence for Approximate Inference (2016) (9)
Text-Based Ideal Points (2020) (9)
The Blessings of Multiple Causes: Rejoinder (2019) (9)
Dose-response modeling in high-throughput cancer drug screenings: an end-to-end approach. (2018) (8)
Population Predictive Checks (2019) (8)
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations (2021) (8)
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data (2018) (8)
Topic Model (2014) (8)
Rejection Sampling Variational Inference (2016) (7)
Computational Analysis and Visualized Comparison of Style in American Poetry (2006) (7)
A Nested HDP for Hierarchical Topic Models (2013) (7)
Mixed Membership Models (2013) (6)
Zero-Inflated Exponential Family Embeddings (2017) (6)
On the Assumptions of Synthetic Control Methods (2021) (6)
Admixtures of latent blocks with application to protein interaction networks (2007) (6)
The Population Posterior and Bayesian Inference on Streams (2015) (6)
A Bayesian Boosting Model (2012) (5)
Technical perspective: Expressive probabilistic models and scalable method of moments (2018) (5)
The Ideal Point Topic Model : Predicting Legislative Roll Calls from Text (2010) (5)
The Stick-Breaking Construction of the Beta Process as a Poisson Process (2011) (5)
Uncovering, understanding, and predicting links (2011) (5)
Deterministic Annealing for Stochastic Variational Inference (2014) (5)
A Proxy Variable View of Shared Confounding (2021) (5)
The Issue-Adjusted Ideal Point Model (2012) (4)
Variational inference with copula augmentation (2015) (4)
Optimization-based Causal Estimation from Heterogenous Environments (2021) (4)
A Filtering Approach to Stochastic Variational Inference (2014) (3)
Evaluating Bayesian Models with Posterior Dispersion Indices (2017) (3)
Counterfactual inference for consumer choice across many product categories (2021) (3)
Probabilistic Conformal Prediction Using Conditional Random Samples (2022) (3)
Identifiable Deep Generative Models via Sparse Decoding (2021) (3)
Adjusting for indirectly measured confounding using large-scale propensity score (2021) (3)
An Analysis of Development of Dementia through the Extended Trajectory Grade of Membership Model (2014) (3)
Unsupervised Representation Learning via Neural Activation Coding (2021) (3)
Linguistic extensions of topic models (2010) (3)
Learning with Reflective Likelihoods (2018) (3)
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport (2022) (3)
Mapping interstellar dust with Gaussian processes (2022) (3)
A Digital Field Experiment Reveals Large Effects of Friend-to-Friend Texting on Voter Turnout (2020) (3)
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference (2021) (2)
Probabilistic Topic Models and User Behavior (2015) (2)
Relational Dose-Response Modeling for Cancer Drug Studies. (2019) (2)
A Bayesian model of dose-response for cancer drug studies (2022) (2)
Comment: Variational Autoencoders as Empirical Bayes (2019) (2)
Dose-response modeling in high-throughput cancer drug screenings: A case study with recommendations for practitioners (2018) (2)
Model-based Classification (2019) (2)
Poisson Trust Factorization for Incorporating Social Networks into Personalized Item Recommendation (2013) (2)
Variational inference and learning for a unified model of syntax, semantics and morphology (2006) (2)
32nd International Conference on Machine Learning : (ICML 2015) : Lile, France, 6-11 July 2015 (2016) (2)
Double Empirical Bayes Testing (2020) (2)
Reconstructing the universe with variational self-boosted sampling (2022) (1)
Skill Rating for Multiplayer Games. Introducing Hypernode Graphs and their Spectral Theory (2020) (1)
Interpretability Constraints and Trade-offs in Using Mixed Membership Models (2014) (1)
Comment (2017) (1)
Nonparametric Mixed Membership Modelling Using the IBP Compound Dirichlet Process (2011) (1)
Profile Predictive Inference (2014) (1)
Probabilistic Topic Models : Origins and Challenges (2014) (1)
Statistical discovery of signaling pathways from an ensemble of weakly informative data sources (2007) (1)
Learning Transferrable Representations of Career Trajectories for Economic Prediction (2022) (1)
A focus on graphical model design and applications to document and image analysis ) (2010) (1)
Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections (2021) (1)
Posterior Predictive Null Checks (2021) (1)
The Markov link method: a nonparametric approach to combine observations from multiple experiments (2018) (1)
Discussion of "Fast Approximate Inference for Arbitrarily Large Semiparametric Regression Models via Message Passing" (2016) (1)
Multicanonical Stochastic Variational Inference (2014) (1)
Starfysh reveals heterogeneous spatial dynamics in the breast tumor microenvironment (2022) (1)
Modeling Influence in Text Corpora (2009) (1)
Heterogeneous Supervised Topic Models (2022) (1)
The $\chi$-Divergence for Approximate Inference (2016) (1)
A general linear-time inference method for Gaussian Processes on one dimension (2021) (1)
The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs) (2019) (1)
The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019) (2019) (1)
Dynamic and Supervised Topic Models for Literature-Based Discovery (2011) (1)
Noise-Based Regularizers for Recurrent Neural Networks (2018) (1)
Bayesian Nonparametric Models (2015) (0)
Interpreting Mixed Membership Models : Implications of Erosheva ’ s Representation Theorem (2018) (0)
Hierarchical bayesian modeling: efficient inference and applications (2012) (0)
Supplementary Materials for Distance Dependent Infinite Latent Feature Models (2014) (0)
Variational Sequential Monte (2018) (0)
Overlapping clustering methods for networks (2019) (0)
PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks (2007) (0)
Posterior Dispersion Indices (2016) (0)
Foundations of Graphical Models (2014) (0)
Hierarchical Dirichlet Processes Author ( s ) : (2011) (0)
Statistical Models (2009) (0)
Posterio Collapse and Latent Variable Non-identiﬁability (2020) (0)
Variational inference in a truncated Dirichlet process (2003) (0)
Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices (2019) (0)
3 0 M ay 2 01 9 Multiple Causes : A Causal Graphical View (2019) (0)
The Multi-Outcome Medical Deconfounder: Assessing Treatment Effect on Multiple Renal Measures (2020) (0)
Causal inference from text: A commentary (2022) (0)
Factor Topographic Latent Source Analysis : Factor Analysis for Brain Images ? (2012) (0)
Adjusting for Unobserved Confounding Using Large-Scale Propensity Scores (2021) (0)
Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations (2020) (0)
Comment: A Discussion of “Nonparametric Bayes Modeling of Populations of Networks” (2017) (0)
Proceedings of the 2006 conference on Statistical network analysis (2006) (0)
1 Introduction to Mixed Membership Models and Methods (2014) (0)
Appendix to Variational Inference: A Review for Statisticians (2017) (0)
Dimension Reduction (2014) (0)
A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making (2022) (0)
Variational Inference for Infinitely Deep Neural Networks (2022) (0)
Comment (2013) (0)
use a Gaussian random walk to capture drift in the underlying language model ; for example (2018) (0)
NON-PARAMETRIC BAYESIAN ANALYSIS OF HETEROGENEOUS DATA (2013) (0)
Exponential Families (2018) (0)
Invariant Representation Learning for Treatment Effect Estimation — Supplementary Material (2021) (0)
Correlated Random Measures: Appendix (2016) (0)
HVMs extend the applicability of normalizing flows to discrete variables. We can also place a distribution over transformations to build an HVM without Jacobians (2). (2015) (0)
Scalable Topic Modeling: Online Learning, Diagnostics, and Recommendation (2017) (0)
Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse (2022) (0)
Assessing the Effects of Friend-to-Friend Texting on Turnout in the 2020 U.S. Presidential Election (2021) (0)
A probabilistic approach to full-brain functional connectivity analyses (2014) (0)
An Invariant Learning Characterization of Controlled Text Generation (2022) (0)
Syntactic Topic Models Supplement (2009) (0)
Generative Models for Decoding Real-Valued Natural Experience in FMRI (2006) (0)
CAREER: Transfer Learning for Economic Prediction of Labor Sequence Data (2022) (0)
v 3 COMS E 6998-002 : Probabilistic Modeling for Discrete Data Lecture 3 : Word Embeddings III Instructor (0)
Extracting information from high-dimensional data: probabilistic modeling, inference and evaluation (2012) (0)
A Probabilistic Model of Cardiac Physiology and Electrocardiograms (2018) (0)
Estimating Social Influence from Observational Data (2022) (0)
Measuring discursive inﬂuence across scholarship (2018) (0)
Comment (2016) (0)
Index of authors, volume 170, 2007 (2007) (0)
Correlated RandomMeasures Rajesh Ranganatha (2018) (0)
A dynamic theory of social failure in isolated communities (2007) (0)
An Approach to Discovery and Re-ranking of Educational content from the World Wide Web using Latent Dirichlet Allocation (2017) (0)
On the Misspecification of Linear Assumptions in Synthetic Control (2023) (0)
Stochastic Search with an Observable State Variable (2010) (0)
The Posterior Predictive Null (2021) (0)
PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks (2007) (0)
Word2net: Deep Representations of Language (2018) (0)

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Published Works

Other Resources About David Blei

What Schools Are Affiliated With David Blei?

David Blei's AcademicInfluence.com Rankings