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ORCIDDavid M. Blei
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- affiliation: Columbia University, New York City, USA
- award: ACM Prize in Computing, 2013
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2020 – today
- 2024
[j37]Linying Zhang
, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noémie Elhadad, David M. Blei, George Hripcsak:
Causal fairness assessment of treatment allocation with electronic health records. J. Biomed. Informatics 155: 104656 (2024)- [j36]Mingzhang Yin, Yixin Wang, David M. Blei:
Optimization-based Causal Estimation from Heterogeneous Environments. J. Mach. Learn. Res. 25: 168:1-168:44 (2024) - [j35]Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei:
CAREER: A Foundation Model for Labor Sequence Data. Trans. Mach. Learn. Res. 2024 (2024) - [c152]Yookoon Park, David M. Blei:
Density Uncertainty Layers for Reliable Uncertainty Estimation. AISTATS 2024: 163-171 - [c151]Achille O. R. Nazaret, Claudia Shi, David M. Blei:
On the Misspecification of Linear Assumptions in Synthetic Controls. AISTATS 2024: 3790-3798 - [c150]Caterina De Bacco, Yixin Wang, David M. Blei:
A causality-inspired plus-minus model for player evaluation in team sports. CLeaR 2024: 769-792 - [c149]Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. ICML 2024 - [c148]Achille Nazaret, Justin Hong, Elham Azizi, David M. Blei:
Stable Differentiable Causal Discovery. ICML 2024 - [i104]Diana Cai, Chirag Modi, Loucas Pillaud-Vivien, Charles C. Margossian, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Batch and match: black-box variational inference with a score-based divergence. CoRR abs/2402.14758 (2024) - [i103]Bohan Wu, David M. Blei:
Extending Mean-Field Variational Inference via Entropic Regularization: Theory and Computation. CoRR abs/2404.09113 (2024) - [i102]Andrew Jesson, Nicolas Beltran-Velez, Quentin Chu, Sweta Karlekar, Jannik Kossen, Yarin Gal, John P. Cunningham, David M. Blei:
Estimating the Hallucination Rate of Generative AI. CoRR abs/2406.07457 (2024) - [i101]Nicolas Beltran-Velez, Alessandro Antonio Grande, Achille Nazaret, Alp Kucukelbir, David M. Blei:
Treeffuser: Probabilistic Predictions via Conditional Diffusions with Gradient-Boosted Trees. CoRR abs/2406.07658 (2024) - [i100]Keyon Vafa, Susan Athey, David M. Blei:
Estimating Wage Disparities Using Foundation Models. CoRR abs/2409.09894 (2024) - 2023
- [j34]Yixin Wang, Dhanya Sridhar, David M. Blei:
Adjusting Machine Learning Decisions for Equal Opportunity and Counterfactual Fairness. Trans. Mach. Learn. Res. 2023 (2023) - [j33]Liyi Zhang, David M. Blei, Christian A. Naesseth:
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. Trans. Mach. Learn. Res. 2023 (2023) - [j32]Carolina Zheng, Keyon Vafa, David M. Blei:
Revisiting Topic-Guided Language Models. Trans. Mach. Learn. Res. 2023 (2023) - [c147]Carolina Zheng, Claudia Shi, Keyon Vafa, Amir Feder, David M. Blei:
An Invariant Learning Characterization of Controlled Text Generation. ACL (1) 2023: 3186-3206 - [c146]Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei:
Probabilistic Conformal Prediction Using Conditional Random Samples. AISTATS 2023: 8814-8836 - [c145]Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei:
Causal-structure Driven Augmentations for Text OOD Generalization. NeurIPS 2023 - [c144]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. NeurIPS 2023 - [c143]Chirag Modi, Robert M. Gower, Charles Margossian, Yuling Yao, David M. Blei, Lawrence K. Saul:
Variational Inference with Gaussian Score Matching. NeurIPS 2023 - [c142]Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei:
Evaluating the Moral Beliefs Encoded in LLMs. NeurIPS 2023 - [c141]Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham:
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. NeurIPS 2023 - [i99]Yixin Wang, David M. Blei, John P. Cunningham:
Posterior Collapse and Latent Variable Non-identifiability. CoRR abs/2301.00537 (2023) - [i98]Carolina Zheng, Claudia Shi, Keyon Vafa, Amir Feder, David M. Blei:
An Invariant Learning Characterization of Controlled Text Generation. CoRR abs/2306.00198 (2023) - [i97]Julius von Kügelgen, Michel Besserve, Wendong Liang, Luigi Gresele, Armin Kekic, Elias Bareinboim, David M. Blei, Bernhard Schölkopf:
Nonparametric Identifiability of Causal Representations from Unknown Interventions. CoRR abs/2306.00542 (2023) - [i96]Yookoon Park, David M. Blei:
Density Uncertainty Layers for Reliable Uncertainty Estimation. CoRR abs/2306.12497 (2023) - [i95]Luhuan Wu, Brian L. Trippe, Christian A. Naesseth, David M. Blei, John P. Cunningham:
Practical and Asymptotically Exact Conditional Sampling in Diffusion Models. CoRR abs/2306.17775 (2023) - [i94]Chirag Modi, Charles Margossian, Yuling Yao, Robert M. Gower, David M. Blei, Lawrence K. Saul:
Variational Inference with Gaussian Score Matching. CoRR abs/2307.07849 (2023) - [i93]Charles C. Margossian, David M. Blei:
Amortized Variational Inference: When and Why? CoRR abs/2307.11018 (2023) - [i92]Nino Scherrer, Claudia Shi, Amir Feder, David M. Blei:
Evaluating the Moral Beliefs Encoded in LLMs. CoRR abs/2307.14324 (2023) - [i91]David M. Kaplan, David M. Blei:
A Computational Approach to Style in American Poetry. CoRR abs/2310.09357 (2023) - [i90]Amir Feder, Yoav Wald, Claudia Shi, Suchi Saria, David M. Blei:
Causal-structure Driven Augmentations for Text OOD Generalization. CoRR abs/2310.12803 (2023) - [i89]Achille Nazaret, Justin Hong, Elham Azizi, David M. Blei:
Stable Differentiable Causal Discovery. CoRR abs/2311.10263 (2023) - [i88]Carolina Zheng, Keyon Vafa, David M. Blei:
Revisiting Topic-Guided Language Models. CoRR abs/2312.02331 (2023) - 2022
- [j31]Linying Zhang, Yixin Wang, Martijn J. Schuemie, David M. Blei, George Hripcsak:
Adjusting for indirectly measured confounding using large-scale propensity score. J. Biomed. Informatics 134: 104204 (2022) - [j30]Wesley Tansey, Victor Veitch, Haoran Zhang, Raul Rabadan, David M. Blei:
The Holdout Randomization Test for Feature Selection in Black Box Models. J. Comput. Graph. Stat. 31(1): 151-162 (2022) - [j29]Dhanya Sridhar, Hal Daumé III, David M. Blei:
Heterogeneous Supervised Topic Models. Trans. Assoc. Comput. Linguistics 10: 732-745 (2022) - [j28]Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei:
Identifiable Deep Generative Models via Sparse Decoding. Trans. Mach. Learn. Res. 2022 (2022) - [c140]Claudia Shi, Dhanya Sridhar, Vishal Misra, David M. Blei:
On the Assumptions of Synthetic Control Methods. AISTATS 2022: 7163-7175 - [c139]Dhanya Sridhar, Caterina De Bacco, David M. Blei:
Estimating Social Influence from Observational Data. CLeaR 2022: 712-733 - [c138]Achille Nazaret, David M. Blei:
Variational Inference for Infinitely Deep Neural Networks. ICML 2022: 16447-16461 - [c137]Sachit Menon, David M. Blei, Carl Vondrick:
Forget-me-not! Contrastive critics for mitigating posterior collapse. UAI 2022: 1360-1370 - [i87]Liyi Zhang, Christian A. Naesseth, David M. Blei:
Transport Score Climbing: Variational Inference Using Forward KL and Adaptive Neural Transport. CoRR abs/2202.01841 (2022) - [i86]Keyon Vafa, Emil Palikot, Tianyu Du, Ayush Kanodia, Susan Athey, David M. Blei:
Learning Transferrable Representations of Career Trajectories for Economic Prediction. CoRR abs/2202.08370 (2022) - [i85]Dhanya Sridhar, Caterina De Bacco, David M. Blei:
Estimating Social Influence from Observational Data. CoRR abs/2204.01633 (2022) - [i84]Zhendong Wang, Ruijiang Gao, Mingzhang Yin, Mingyuan Zhou, David M. Blei:
Probabilistic Conformal Prediction Using Conditional Random Samples. CoRR abs/2206.06584 (2022) - [i83]Sachit Menon, David M. Blei, Carl Vondrick:
Forget-me-not! Contrastive Critics for Mitigating Posterior Collapse. CoRR abs/2207.09535 (2022) - [i82]Achille Nazaret, David M. Blei:
Variational Inference for Infinitely Deep Neural Networks. CoRR abs/2209.10091 (2022) - [i81]Linying Zhang, Lauren R. Richter, Yixin Wang, Anna Ostropolets, Noemie Elhadad, David M. Blei, George Hripcsak:
A Bayesian Causal Inference Approach for Assessing Fairness in Clinical Decision-Making. CoRR abs/2211.11183 (2022) - 2021
- [j27]Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski:
A general linear-time inference method for Gaussian Processes on one dimension. J. Mach. Learn. Res. 22: 234:1-234:36 (2021) - [c136]Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham:
Hierarchical Inducing Point Gaussian Process for Inter-domian Observations. AISTATS 2021: 2926-2934 - [c135]Keyon Vafa, Yuntian Deng, David M. Blei, Alexander M. Rush:
Rationales for Sequential Predictions. EMNLP (1) 2021: 10314-10332 - [c134]Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei:
Unsupervised Representation Learning via Neural Activation Coding. ICML 2021: 8391-8400 - [c133]Yixin Wang, David M. Blei:
A Proxy Variable View of Shared Confounding. ICML 2021: 10697-10707 - [c132]Yixin Wang, David M. Blei, John P. Cunningham:
Posterior Collapse and Latent Variable Non-identifiability. NeurIPS 2021: 5443-5455 - [c131]Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David M. Blei, Itsik Pe'er:
variational combinatorial sequential monte carlo methods for bayesian phylogenetic inference. UAI 2021: 971-981 - [c130]Claudia Shi, Victor Veitch, David M. Blei:
Invariant representation learning for treatment effect estimation. UAI 2021: 1546-1555 - [c129]Aaron Schein, Keyon Vafa, Dhanya Sridhar, Victor Veitch, Jeffrey Quinn, James Moffet, David M. Blei, Donald P. Green:
Assessing the Effects of Friend-to-Friend Texting onTurnout in the 2018 US Midterm Elections. WWW 2021: 2025-2036 - [i80]Luhuan Wu, Andrew Miller, Lauren Anderson, Geoff Pleiss, David M. Blei, John P. Cunningham:
Hierarchical Inducing Point Gaussian Process for Inter-domain Observations. CoRR abs/2103.00393 (2021) - [i79]Antonio Khalil Moretti, Liyi Zhang, Christian A. Naesseth, Hadiah Venner, David M. Blei, Itsik Pe'er:
Variational Combinatorial Sequential Monte Carlo Methods for Bayesian Phylogenetic Inference. CoRR abs/2106.00075 (2021) - [i78]Keyon Vafa, Yuntian Deng, David M. Blei, Alexander M. Rush:
Rationales for Sequential Predictions. CoRR abs/2109.06387 (2021) - [i77]Mingzhang Yin, Yixin Wang, David M. Blei:
Optimization-based Causal Estimation from Heterogenous Environments. CoRR abs/2109.11990 (2021) - [i76]Gemma E. Moran, Dhanya Sridhar, Yixin Wang, David M. Blei:
Identifiable Variational Autoencoders via Sparse Decoding. CoRR abs/2110.10804 (2021) - [i75]Yookoon Park, Sangho Lee, Gunhee Kim, David M. Blei:
Unsupervised Representation Learning via Neural Activation Coding. CoRR abs/2112.04014 (2021) - 2020
- [j26]Adji Bousso Dieng, Francisco J. R. Ruiz, David M. Blei:
Topic Modeling in Embedding Spaces. Trans. Assoc. Comput. Linguistics 8: 439-453 (2020) - [c128]Keyon Vafa, Suresh Naidu, David M. Blei:
Text-Based Ideal Points. ACL 2020: 5345-5357 - [c127]George Hripcsak, David M. Blei, Elias Bareinboim, Martijn J. Schuemie, Linying Zhang:
Causal Inference from Observational Healthcare Data: Implications, Impacts and Innovations. AMIA 2020 - [c126]Linying Zhang, Yixin Wang, Anna Ostropolets, Ruijun Chen, David M. Blei, George Hripcsak:
The Multi-Outcome Medical Deconfounder: Assessing Treatment Effect on Multiple Renal Measures. AMIA 2020 - [c125]Christian A. Naesseth, Fredrik Lindsten, David M. Blei:
Markovian Score Climbing: Variational Inference with KL(p||q). NeurIPS 2020 - [c124]Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei:
Causal Inference for Recommender Systems. RecSys 2020: 426-431 - [c123]Victor Veitch, Dhanya Sridhar, David M. Blei:
Adapting Text Embeddings for Causal Inference. UAI 2020: 919-928 - [i74]Yixin Wang, David M. Blei:
Towards Clarifying the Theory of the Deconfounder. CoRR abs/2003.04948 (2020) - [i73]Jackson Loper, David M. Blei, John P. Cunningham, Liam Paninski:
General linear-time inference for Gaussian Processes on one dimension. CoRR abs/2003.05554 (2020) - [i72]Christian A. Naesseth, Fredrik Lindsten, David M. Blei:
Markovian Score Climbing: Variational Inference with KL(p||q). CoRR abs/2003.10374 (2020) - [i71]Keyon Vafa, Suresh Naidu, David M. Blei:
Text-Based Ideal Points. CoRR abs/2005.04232 (2020) - [i70]Claudia Shi, Victor Veitch, David M. Blei:
Invariant Representation Learning for Treatment Effect Estimation. CoRR abs/2011.12379 (2020)
2010 – 2019
- 2019
- [c122]Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. AISTATS 2019: 1733-1742 - [c121]Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei:
Avoiding Latent Variable Collapse with Generative Skip Models. AISTATS 2019: 2397-2405 - [c120]Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak:
The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records. MLHC 2019: 490-512 - [c119]Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Poisson-Randomized Gamma Dynamical Systems. NeurIPS 2019: 781-792 - [c118]Claudia Shi, David M. Blei, Victor Veitch:
Adapting Neural Networks for the Estimation of Treatment Effects. NeurIPS 2019: 2503-2513 - [c117]Yixin Wang, David M. Blei:
Variational Bayes under Model Misspecification. NeurIPS 2019: 13357-13367 - [c116]Victor Veitch, Yixin Wang, David M. Blei:
Using Embeddings to Correct for Unobserved Confounding in Networks. NeurIPS 2019: 13769-13779 - [i69]Victor Veitch, Yixin Wang, David M. Blei:
Using Embeddings to Correct for Unobserved Confounding. CoRR abs/1902.04114 (2019) - [i68]Linying Zhang, Yixin Wang, Anna Ostropolets, Jami J. Mulgrave, David M. Blei, George Hripcsak:
The Medical Deconfounder: Assessing Treatment Effect with Electronic Health Records (EHRs). CoRR abs/1904.02098 (2019) - [i67]Yixin Wang, David M. Blei:
Variational Bayes under Model Misspecification. CoRR abs/1905.10859 (2019) - [i66]Yixin Wang, Dhanya Sridhar, David M. Blei:
Equal Opportunity and Affirmative Action via Counterfactual Predictions. CoRR abs/1905.10870 (2019) - [i65]Victor Veitch, Dhanya Sridhar, David M. Blei:
Using Text Embeddings for Causal Inference. CoRR abs/1905.12741 (2019) - [i64]Yixin Wang, David M. Blei:
Multiple Causes: A Causal Graphical View. CoRR abs/1905.12793 (2019) - [i63]Claudia Shi, David M. Blei, Victor Veitch:
Adapting Neural Networks for the Estimation of Treatment Effects. CoRR abs/1906.02120 (2019) - [i62]Robert Donnelly, Francisco J. R. Ruiz, David M. Blei, Susan Athey:
Counterfactual Inference for Consumer Choice Across Many Product Categories. CoRR abs/1906.02635 (2019) - [i61]Wesley Tansey, Christopher Tosh, David M. Blei:
Bayesian Tensor Filtering: Smooth, Locally-Adaptive Factorization of Functional Matrices. CoRR abs/1906.04072 (2019) - [i60]Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei:
Topic Modeling in Embedding Spaces. CoRR abs/1907.04907 (2019) - [i59]Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei:
The Dynamic Embedded Topic Model. CoRR abs/1907.05545 (2019) - [i58]Rajesh Ranganath, David M. Blei:
Population Predictive Checks. CoRR abs/1908.00882 (2019) - [i57]Adji B. Dieng, Francisco J. R. Ruiz, David M. Blei, Michalis K. Titsias:
Prescribed Generative Adversarial Networks. CoRR abs/1910.04302 (2019) - [i56]Yixin Wang, David M. Blei:
The Blessings of Multiple Causes: A Reply to Ogburn et al. (2019). CoRR abs/1910.07320 (2019) - [i55]Aaron Schein, Scott W. Linderman, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Poisson-Randomized Gamma Dynamical Systems. CoRR abs/1910.12991 (2019) - 2018
- [j25]David M. Blei:
Technical perspective: Expressive probabilistic models and scalable method of moments. Commun. ACM 61(4): 84 (2018) - [j24]Jeremy R. Manning, Xia Zhu, Theodore L. Willke, Rajesh Ranganath, Kimberly L. Stachenfeld, Uri Hasson, David M. Blei, Kenneth A. Norman:
A probabilistic approach to discovering dynamic full-brain functional connectivity patterns. NeuroImage 180(Part): 243-252 (2018) - [c115]Christian A. Naesseth, Scott W. Linderman, Rajesh Ranganath, David M. Blei:
Variational Sequential Monte Carlo. AISTATS 2018: 968-977 - [c114]Jaan Altosaar, Rajesh Ranganath, David M. Blei:
Proximity Variational Inference. AISTATS 2018: 1961-1969 - [c113]Dustin Tran, David M. Blei:
Implicit Causal Models for Genome-wide Association Studies. ICLR (Poster) 2018 - [c112]Adji Bousso Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei:
Noisin: Unbiased Regularization for Recurrent Neural Networks. ICML 2018: 1251-1260 - [c111]Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei:
Augment and Reduce: Stochastic Inference for Large Categorical Distributions. ICML 2018: 4400-4409 - [c110]Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan:
Black Box FDR. ICML 2018: 4874-4883 - [c109]Maja Rudolph, David M. Blei:
Dynamic Embeddings for Language Evolution. WWW 2018: 1003-1011 - [i54]Susan Athey, David M. Blei, Robert Donnelly, Francisco J. R. Ruiz, Tobias Schmidt:
Estimating Heterogeneous Consumer Preferences for Restaurants and Travel Time Using Mobile Location Data. CoRR abs/1801.07826 (2018) - [i53]Francisco J. R. Ruiz, Michalis K. Titsias, Adji B. Dieng, David M. Blei:
Augment and Reduce: Stochastic Inference for Large Categorical Distributions. CoRR abs/1802.04220 (2018) - [i52]Kriste Krstovski, David M. Blei:
Equation Embeddings. CoRR abs/1803.09123 (2018) - [i51]Adji B. Dieng, Rajesh Ranganath, Jaan Altosaar, David M. Blei:
Noisin: Unbiased Regularization for Recurrent Neural Networks. CoRR abs/1805.01500 (2018) - [i50]Yixin Wang, David M. Blei:
The Blessings of Multiple Causes. CoRR abs/1805.06826 (2018) - [i49]Wesley Tansey, Yixin Wang, David M. Blei, Raul Rabadan:
Black Box FDR. CoRR abs/1806.03143 (2018) - [i48]Victor Veitch, Morgane Austern, Wenda Zhou, David M. Blei, Peter Orbanz:
Empirical Risk Minimization and Stochastic Gradient Descent for Relational Data. CoRR abs/1806.10701 (2018) - [i47]Adji B. Dieng, Yoon Kim, Alexander M. Rush, David M. Blei:
Avoiding Latent Variable Collapse With Generative Skip Models. CoRR abs/1807.04863 (2018) - [i46]Yixin Wang, Dawen Liang, Laurent Charlin, David M. Blei:
The Deconfounded Recommender: A Causal Inference Approach to Recommendation. CoRR abs/1808.06581 (2018) - [i45]Andrew C. Miller, Ziad Obermeyer, David M. Blei, John P. Cunningham, Sendhil Mullainathan:
A Probabilistic Model of Cardiac Physiology and Electrocardiograms. CoRR abs/1812.00209 (2018) - 2017
- [j23]Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Differentiation Variational Inference. J. Mach. Learn. Res. 18: 14:1-14:45 (2017) - [j22]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
Stochastic Gradient Descent as Approximate Bayesian Inference. J. Mach. Learn. Res. 18: 134:1-134:35 (2017) - [j21]David M. Blei, Padhraic Smyth:
Science and data science. Proc. Natl. Acad. Sci. USA 114(33): 8689-8692 (2017) - [c108]Christian A. Naesseth, Francisco J. R. Ruiz, Scott W. Linderman, David M. Blei:
Reparameterization Gradients through Acceptance-Rejection Sampling Algorithms. AISTATS 2017: 489-498 - [c107]Scott W. Linderman, Matthew J. Johnson, Andrew C. Miller, Ryan P. Adams, David M. Blei, Liam Paninski:
Bayesian Learning and Inference in Recurrent Switching Linear Dynamical Systems. AISTATS 2017: 914-922 - [c106]Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei:
Deep Probabilistic Programming. ICLR (Poster) 2017 - [c105]Alp Kucukelbir, Yixin Wang, David M. Blei:
Evaluating Bayesian Models with Posterior Dispersion Indices. ICML 2017: 1925-1934 - [c104]Li-Ping Liu, David M. Blei:
Zero-Inflated Exponential Family Embeddings. ICML 2017: 2140-2148 - [c103]Yixin Wang, Alp Kucukelbir, David M. Blei:
Robust Probabilistic Modeling with Bayesian Data Reweighting. ICML 2017: 3646-3655 - [c102]Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Structured Embedding Models for Grouped Data. NIPS 2017: 251-261 - [c101]Adji Bousso Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei:
Variational Inference via \chi Upper Bound Minimization. NIPS 2017: 2732-2741 - [c100]Li-Ping Liu, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Context Selection for Embedding Models. NIPS 2017: 4816-4825 - [c99]Dustin Tran, Rajesh Ranganath, David M. Blei:
Hierarchical Implicit Models and Likelihood-Free Variational Inference. NIPS 2017: 5523-5533 - [i44]Dustin Tran, Matthew D. Hoffman, Rif A. Saurous, Eugene Brevdo, Kevin Murphy, David M. Blei:
Deep Probabilistic Programming. CoRR abs/1701.03757 (2017) - [i43]Dustin Tran, Rajesh Ranganath, David M. Blei:
Deep and Hierarchical Implicit Models. CoRR abs/1702.08896 (2017) - [i42]Maja Rudolph, David M. Blei:
Dynamic Bernoulli Embeddings for Language Evolution. CoRR abs/1703.08052 (2017) - [i41]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
Stochastic Gradient Descent as Approximate Bayesian Inference. CoRR abs/1704.04289 (2017) - [i40]Yixin Wang, David M. Blei:
Frequentist Consistency of Variational Bayes. CoRR abs/1705.03439 (2017) - [i39]Jaan Altosaar, Rajesh Ranganath, David M. Blei:
Proximity Variational Inference. CoRR abs/1705.08931 (2017) - [i38]Maja Rudolph, Francisco J. R. Ruiz, Susan Athey, David M. Blei:
Structured Embedding Models for Grouped Data. CoRR abs/1709.10367 (2017) - [i37]Dustin Tran, David M. Blei:
Implicit Causal Models for Genome-wide Association Studies. CoRR abs/1710.10742 (2017) - [i36]Francisco J. R. Ruiz, Susan Athey, David M. Blei:
SHOPPER: A Probabilistic Model of Consumer Choice with Substitutes and Complements. CoRR abs/1711.03560 (2017) - 2016
- [c98]Stephan Mandt, James McInerney, Farhan Abrol, Rajesh Ranganath, David M. Blei:
Variational Tempering. AISTATS 2016: 704-712 - [c97]Allison June-Barlow Chaney, Hanna M. Wallach, Matthew Connelly, David M. Blei:
Detecting and Characterizing Events. EMNLP 2016: 1142-1152 - [c96]Rajesh Ranganath, Dustin Tran, David M. Blei:
Hierarchical Variational Models. ICML 2016: 324-333 - [c95]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
A Variational Analysis of Stochastic Gradient Algorithms. ICML 2016: 354-363 - [c94]Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. ICML 2016: 2810-2819 - [c93]Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei:
Deep Survival Analysis. MLHC 2016: 101-114 - [c92]Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei:
The Generalized Reparameterization Gradient. NIPS 2016: 460-468 - [c91]Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei:
Exponential Family Embeddings. NIPS 2016: 478-486 - [c90]Rajesh Ranganath, Dustin Tran, Jaan Altosaar, David M. Blei:
Operator Variational Inference. NIPS 2016: 496-504 - [c89]Dawen Liang, Jaan Altosaar, Laurent Charlin, David M. Blei:
Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence. RecSys 2016: 59-66 - [c88]Francisco J. R. Ruiz, Michalis K. Titsias, David M. Blei:
Overdispersed Black-Box Variational Inference. UAI 2016 - [c87]Dawen Liang, Laurent Charlin, James McInerney, David M. Blei:
Modeling User Exposure in Recommendation. WWW 2016: 951-961 - [c86]Maja R. Rudolph, Joseph G. Ellis, David M. Blei:
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve. WWW 2016: 1113-1122 - [c85]Dustin Tran, Rajesh Ranganath, David M. Blei:
Variational Gaussian Process. ICLR 2016 - [i35]David M. Blei, Alp Kucukelbir, Jon D. McAuliffe:
Variational Inference: A Review for Statisticians. CoRR abs/1601.00670 (2016) - [i34]Stephan Mandt, Matthew D. Hoffman, David M. Blei:
A Variational Analysis of Stochastic Gradient Algorithms. CoRR abs/1602.02666 (2016) - [i33]Alp Kucukelbir, Dustin Tran, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Differentiation Variational Inference. CoRR abs/1603.00788 (2016) - [i32]Alp Kucukelbir, David M. Blei:
Posterior Dispersion Indices. CoRR abs/1605.07604 (2016) - [i31]Aaron Schein, Mingyuan Zhou, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tucker Decomposition for Learning the Structure of International Relations. CoRR abs/1606.01855 (2016) - [i30]Yixin Wang, Alp Kucukelbir, David M. Blei:
Reweighted Data for Robust Probabilistic Models. CoRR abs/1606.03860 (2016) - [i29]Maja Rudolph, Francisco J. R. Ruiz, Stephan Mandt, David M. Blei:
Exponential Family Embeddings. CoRR abs/1608.00778 (2016) - [i28]Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei:
Deep Survival Analysis. CoRR abs/1608.02158 (2016) - [i27]Rajesh Ranganath, Jaan Altosaar, Dustin Tran, David M. Blei:
Operator Variational Inference. CoRR abs/1610.09033 (2016) - [i26]Dustin Tran, Alp Kucukelbir, Adji B. Dieng, Maja Rudolph, Dawen Liang, David M. Blei:
Edward: A library for probabilistic modeling, inference, and criticism. CoRR abs/1610.09787 (2016) - [i25]Adji B. Dieng, Dustin Tran, Rajesh Ranganath, John W. Paisley, David M. Blei:
The $χ$-Divergence for Approximate Inference. CoRR abs/1611.00328 (2016) - 2015
- [j20]Adler J. Perotte, Rajesh Ranganath, Jamie S. Hirsch, David M. Blei, Noémie Elhadad:
Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis. J. Am. Medical Informatics Assoc. 22(4): 872-880 (2015) - [j19]John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan:
Nested Hierarchical Dirichlet Processes. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 256-270 (2015) - [j18]Samuel Gershman, Peter I. Frazier, David M. Blei:
Distance Dependent Infinite Latent Feature Models. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 334-345 (2015) - [j17]Gungor Polatkan, Mingyuan Zhou, Lawrence Carin, David M. Blei, Ingrid Daubechies:
A Bayesian Nonparametric Approach to Image Super-Resolution. IEEE Trans. Pattern Anal. Mach. Intell. 37(2): 346-358 (2015) - [c84]Matthew D. Hoffman, David M. Blei:
Stochastic Structured Variational Inference. AISTATS 2015 - [c83]Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei:
Deep Exponential Families. AISTATS 2015 - [c82]Aaron Schein, John W. Paisley, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. KDD 2015: 1045-1054 - [c81]Alp Kucukelbir, Rajesh Ranganath, Andrew Gelman, David M. Blei:
Automatic Variational Inference in Stan. NIPS 2015: 568-576 - [c80]James McInerney, Rajesh Ranganath, David M. Blei:
The Population Posterior and Bayesian Modeling on Streams. NIPS 2015: 1153-1161 - [c79]Dustin Tran, David M. Blei, Edoardo M. Airoldi:
Copula variational inference. NIPS 2015: 3564-3572 - [c78]Allison June-Barlow Chaney, David M. Blei, Tina Eliassi-Rad:
A Probabilistic Model for Using Social Networks in Personalized Item Recommendation. RecSys 2015: 43-50 - [c77]Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei:
Dynamic Poisson Factorization. RecSys 2015: 155-162 - [c76]Prem Gopalan, Jake M. Hofman, David M. Blei:
Scalable Recommendation with Hierarchical Poisson Factorization. UAI 2015: 326-335 - [c75]Alp Kucukelbir, David M. Blei:
Population Empirical Bayes. UAI 2015: 444-453 - [c74]Rajesh Ranganath, Adler J. Perotte, Noémie Elhadad, David M. Blei:
The Survival Filter: Joint Survival Analysis with a Latent Time Series. UAI 2015: 742-751 - [e3]Francis R. Bach, David M. Blei:
Proceedings of the 32nd International Conference on Machine Learning, ICML 2015, Lille, France, 6-11 July 2015. JMLR Workshop and Conference Proceedings 37, JMLR.org 2015 [contents] - [i24]Dustin Tran, David M. Blei, Edoardo M. Airoldi:
Variational inference with copula augmentation. CoRR abs/1506.03159 (2015) - [i23]Aaron Schein, John W. Paisley, David M. Blei, Hanna M. Wallach:
Bayesian Poisson Tensor Factorization for Inferring Multilateral Relations from Sparse Dyadic Event Counts. CoRR abs/1506.03493 (2015) - [i22]Maja R. Rudolph, Joseph G. Ellis, David M. Blei:
Objective Variables for Probabilistic Revenue Maximization in Second-Price Auctions with Reserve. CoRR abs/1506.07504 (2015) - [i21]Laurent Charlin, Rajesh Ranganath, James McInerney, David M. Blei:
Dynamic Poisson Factorization. CoRR abs/1509.04640 (2015) - [i20]Dawen Liang, Laurent Charlin, James McInerney, David M. Blei:
Modeling User Exposure in Recommendation. CoRR abs/1510.07025 (2015) - [i19]Rajesh Ranganath, Dustin Tran, David M. Blei:
Hierarchical Variational Models. CoRR abs/1511.02386 (2015) - 2014
- [j16]Samuel Gershman, David M. Blei, Kenneth A. Norman, Per B. Sederberg:
Decomposing spatiotemporal brain patterns into topographic latent sources. NeuroImage 98: 91-102 (2014) - [c73]Prem Gopalan, Francisco J. R. Ruiz, Rajesh Ranganath, David M. Blei:
Bayesian Nonparametric Poisson Factorization for Recommendation Systems. AISTATS 2014: 275-283 - [c72]Rajesh Ranganath, Sean Gerrish, David M. Blei:
Black Box Variational Inference. AISTATS 2014: 814-822 - [c71]Maxim Rabinovich, David M. Blei:
The Inverse Regression Topic Model. ICML 2014: 199-207 - [c70]Neil Houlsby, David M. Blei:
A Filtering Approach to Stochastic Variational Inference. NIPS 2014: 2114-2122 - [c69]Stephan Mandt, David M. Blei:
Smoothed Gradients for Stochastic Variational Inference. NIPS 2014: 2438-2446 - [c68]Prem Gopalan, Laurent Charlin, David M. Blei:
Content-based recommendations with Poisson factorization. NIPS 2014: 3176-3184 - [c67]Jeremy R. Manning, Rajesh Ranganath, Waitsang Keung, Nicholas B. Turk-Browne, Jonathan D. Cohen, Kenneth A. Norman, David M. Blei:
Hierarchical topographic factor analysis. PRNI 2014: 1-4 - [e2]Edoardo M. Airoldi, David M. Blei, Elena A. Erosheva, Stephen E. Fienberg:
Handbook of Mixed Membership Models and Their Applications. Chapman and Hall/CRC 2014, ISBN 978-1-4665-0408-0 [contents] - [r2]Edoardo M. Airoldi, David M. Blei, Elena A. Erosheva, Stephen E. Fienberg:
Introduction to Mixed Membership Models and Methods. Handbook of Mixed Membership Models and Their Applications 2014: 3-13 - [r1]John W. Paisley, David M. Blei, Michael I. Jordan:
Bayesian Nonnegative Matrix Factorization with Stochastic Variational Inference. Handbook of Mixed Membership Models and Their Applications 2014: 205-224 - [i18]Rajesh Ranganath, Sean Gerrish, David M. Blei:
Black Box Variational Inference. CoRR abs/1401.0118 (2014) - [i17]Stephan Mandt, David M. Blei:
Smoothed Gradients for Stochastic Variational Inference. CoRR abs/1406.3650 (2014) - [i16]Alp Kucukelbir, David M. Blei:
Profile Predictive Inference. CoRR abs/1411.0292 (2014) - [i15]Farhan Abrol, Stephan Mandt, Rajesh Ranganath, David M. Blei:
Deterministic Annealing for Stochastic Variational Inference. CoRR abs/1411.1810 (2014) - [i14]Rajesh Ranganath, Linpeng Tang, Laurent Charlin, David M. Blei:
Deep Exponential Families. CoRR abs/1411.2581 (2014) - 2013
- [j15]Chong Wang, David M. Blei:
Variational inference in nonconjugate models. J. Mach. Learn. Res. 14(1): 1005-1031 (2013) - [j14]Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley:
Stochastic variational inference. J. Mach. Learn. Res. 14(1): 1303-1347 (2013) - [j13]Bo Chen, Gungor Polatkan, Guillermo Sapiro, David M. Blei, David B. Dunson, Lawrence Carin:
Deep Learning with Hierarchical Convolutional Factor Analysis. IEEE Trans. Pattern Anal. Mach. Intell. 35(8): 1887-1901 (2013) - [j12]Prem Gopalan, David M. Blei:
Efficient discovery of overlapping communities in massive networks. Proc. Natl. Acad. Sci. USA 110(36): 14534-14539 (2013) - [c66]Rajesh Ranganath, Chong Wang, David M. Blei, Eric P. Xing:
An Adaptive Learning Rate for Stochastic Variational Inference. ICML (2) 2013: 298-306 - [c65]Dae Il Kim, Prem Gopalan, David M. Blei, Erik B. Sudderth:
Efficient Online Inference for Bayesian Nonparametric Relational Models. NIPS 2013: 962-970 - [c64]Prem Gopalan, Chong Wang, David M. Blei:
Modeling Overlapping Communities with Node Popularities. NIPS 2013: 2850-2858 - [c63]John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan:
A Nested HDP for Hierarchical Topic Models. ICLR (Workshop) 2013 - [i13]David M. Blei, J. Andrew Bagnell, Andrew McCallum:
Learning with Scope, with Application to Information Extraction and Classification. CoRR abs/1301.0556 (2013) - [i12]Prem Gopalan, Jake M. Hofman, David M. Blei:
Scalable Recommendation with Poisson Factorization. CoRR abs/1311.1704 (2013) - 2012
- [j11]David M. Blei:
Probabilistic topic models. Commun. ACM 55(4): 77-84 (2012) - [c62]Samuel Gershman, Matthew D. Hoffman, David M. Blei:
Nonparametric variational inference. ICML 2012 - [c61]David M. Mimno, Matthew D. Hoffman, David M. Blei:
Sparse stochastic inference for latent Dirichlet allocation. ICML 2012 - [c60]John W. Paisley, David M. Blei, Michael I. Jordan:
Variational Bayesian Inference with Stochastic Search. ICML 2012 - [c59]Allison June-Barlow Chaney, David M. Blei:
Visualizing Topic Models. ICWSM 2012 - [c58]Chong Wang, David M. Blei:
Truncation-free Online Variational Inference for Bayesian Nonparametric Models. NIPS 2012: 422-430 - [c57]Prem Gopalan, David M. Mimno, Sean Gerrish, Michael J. Freedman, David M. Blei:
Scalable Inference of Overlapping Communities. NIPS 2012: 2258-2266 - [c56]Sean Gerrish, David M. Blei:
How They Vote: Issue-Adjusted Models of Legislative Behavior. NIPS 2012: 2762-2770 - [c55]John W. Paisley, David M. Blei, Michael I. Jordan:
Stick-Breaking Beta Processes and the Poisson Process. AISTATS 2012: 850-858 - [i11]Chong Wang, David M. Blei:
A Split-Merge MCMC Algorithm for the Hierarchical Dirichlet Process. CoRR abs/1201.1657 (2012) - [i10]Jordan L. Boyd-Graber, David M. Blei:
Multilingual Topic Models for Unaligned Text. CoRR abs/1205.2657 (2012) - [i9]Chong Wang, David M. Blei, David Heckerman:
Continuous Time Dynamic Topic Models. CoRR abs/1206.3298 (2012) - [i8]Samuel Gershman, Matthew D. Hoffman, David M. Blei:
Nonparametric variational inference. CoRR abs/1206.4665 (2012) - [i7]Wei Li, David M. Blei, Andrew McCallum:
Nonparametric Bayes Pachinko Allocation. CoRR abs/1206.5270 (2012) - [i6]Matthew D. Hoffman, David M. Blei, Chong Wang, John W. Paisley:
Stochastic Variational Inference. CoRR abs/1206.7051 (2012) - [i5]Gungor Polatkan, Mingyuan Zhou, Lawrence Carin, David M. Blei, Ingrid Daubechies:
A Bayesian Nonparametric Approach to Image Super-resolution. CoRR abs/1209.5019 (2012) - [i4]Sean Gerrish, David M. Blei:
The Issue-Adjusted Ideal Point Model. CoRR abs/1209.6004 (2012) - [i3]John W. Paisley, Chong Wang, David M. Blei, Michael I. Jordan:
Nested Hierarchical Dirichlet Processes. CoRR abs/1210.6738 (2012) - 2011
- [j10]Lauren Hannah, David M. Blei, Warren B. Powell:
Dirichlet Process Mixtures of Generalized Linear Models. J. Mach. Learn. Res. 12: 1923-1953 (2011) - [j9]David M. Blei, Peter I. Frazier:
Distance Dependent Chinese Restaurant Processes. J. Mach. Learn. Res. 12: 2461-2488 (2011) - [j8]Samuel Gershman, David M. Blei, Francisco Pereira, Kenneth A. Norman:
A topographic latent source model for fMRI data. NeuroImage 57(1): 89-100 (2011) - [c54]David M. Mimno, David M. Blei:
Bayesian Checking for Topic Models. EMNLP 2011: 227-237 - [c53]Sean Gerrish, David M. Blei:
Predicting Legislative Roll Calls from Text. ICML 2011: 489-496 - [c52]John W. Paisley, Lawrence Carin, David M. Blei:
Variational Inference for Stick-Breaking Beta Process Priors. ICML 2011: 889-896 - [c51]Chong Wang, David M. Blei:
Collaborative topic modeling for recommending scientific articles. KDD 2011: 448-456 - [c50]Soumya Ghosh, Andrei B. Ungureanu, Erik B. Sudderth, David M. Blei:
Spatial distance dependent Chinese restaurant processes for image segmentation. NIPS 2011: 1476-1484 - [c49]John W. Paisley, Chong Wang, David M. Blei:
The Discrete Infinite Logistic Normal Distribution for Mixed-Membership Modeling. AISTATS 2011: 74-82 - [c48]Chong Wang, John W. Paisley, David M. Blei:
Online Variational Inference for the Hierarchical Dirichlet Process. AISTATS 2011: 752-760 - 2010
- [j7]David M. Blei, Thomas L. Griffiths, Michael I. Jordan:
The nested chinese restaurant process and bayesian nonparametric inference of topic hierarchies. J. ACM 57(2): 7:1-7:30 (2010) - [j6]David M. Blei, Lawrence Carin, David B. Dunson:
Probabilistic Topic Models. IEEE Signal Process. Mag. 27(6): 55-65 (2010) - [c47]Li-Jia Li, Chong Wang, Yongwhan Lim, David M. Blei, Li Fei-Fei:
Building and using a semantivisual image hierarchy. CVPR 2010: 3336-3343 - [c46]David M. Blei, Peter I. Frazier:
Distance dependent Chinese restaurant processes. ICML 2010: 87-94 - [c45]Sean Gerrish, David M. Blei:
A Language-based Approach to Measuring Scholarly Impact. ICML 2010: 375-382 - [c44]Matthew D. Hoffman, David M. Blei, Perry R. Cook:
Bayesian Nonparametric Matrix Factorization for Recorded Music. ICML 2010: 439-446 - [c43]Sinead Williamson, Chong Wang, Katherine A. Heller, David M. Blei:
The IBP Compound Dirichlet Process and its Application to Focused Topic Modeling. ICML 2010: 1151-1158 - [c42]Shay B. Cohen, David M. Blei, Noah A. Smith:
Variational Inference for Adaptor Grammars. HLT-NAACL 2010: 564-572 - [c41]Lauren Hannah, Warren B. Powell, David M. Blei:
Nonparametric Density Estimation for Stochastic Optimization with an Observable State Variable. NIPS 2010: 820-828 - [c40]Matthew D. Hoffman, David M. Blei, Francis R. Bach:
Online Learning for Latent Dirichlet Allocation. NIPS 2010: 856-864 - [c39]Lauren Hannah, David M. Blei, Warren B. Powell:
Dirichlet Process Mixtures of Generalized Linear Models. AISTATS 2010: 313-320 - [c38]Alexander Lorbert, David J. Eis, Victoria Kostina, David M. Blei, Peter J. Ramadge:
Exploiting Covariate Similarity in Sparse Regression via the Pairwise Elastic Net. AISTATS 2010: 477-484 - [i2]Jordan L. Boyd-Graber, David M. Blei:
Syntactic Topic Models. CoRR abs/1002.4665 (2010)
2000 – 2009
- 2009
- [c37]Chong Wang, David M. Blei, Li Fei-Fei:
Simultaneous image classification and annotation. CVPR 2009: 1903-1910 - [c36]Matthew D. Hoffman, Perry R. Cook, David M. Blei:
Bayesian Spectral Matching: Turning Young MC into MC Hammer via MCMC Sampling. ICMC 2009 - [c35]Matthew D. Hoffman, David M. Blei, Perry R. Cook:
Easy As CBA: A Simple Probabilistic Model for Tagging Music. ISMIR 2009: 369-374 - [c34]Jonathan D. Chang, Jordan L. Boyd-Graber, David M. Blei:
Connections between the lines: augmenting social networks with text. KDD 2009: 169-178 - [c33]Jonathan D. Chang, Jordan L. Boyd-Graber, Sean Gerrish, Chong Wang, David M. Blei:
Reading Tea Leaves: How Humans Interpret Topic Models. NIPS 2009: 288-296 - [c32]Richard Socher, Samuel Gershman, Adler J. Perotte, Per B. Sederberg, David M. Blei, Kenneth A. Norman:
A Bayesian Analysis of Dynamics in Free Recall. NIPS 2009: 1714-1722 - [c31]Chong Wang, David M. Blei:
Decoupling Sparsity and Smoothness in the Discrete Hierarchical Dirichlet Process. NIPS 2009: 1982-1989 - [c30]Chong Wang, David M. Blei:
Variational Inference for the Nested Chinese Restaurant Process. NIPS 2009: 1990-1998 - [c29]Jordan L. Boyd-Graber, David M. Blei:
Multilingual Topic Models for Unaligned Text. UAI 2009: 75-82 - [c28]Jonathan D. Chang, David M. Blei:
Relational Topic Models for Document Networks. AISTATS 2009: 81-88 - [c27]Chong Wang, Bo Thiesson, Christopher Meek, David M. Blei:
Markov Topic Models. AISTATS 2009: 583-590 - 2008
- [j5]Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing:
Mixed Membership Stochastic Blockmodels. J. Mach. Learn. Res. 9: 1981-2014 (2008) - [c26]Matthew D. Hoffman, Perry R. Cook, David M. Blei:
Data-Driven Recomposition using the Hierarchical Dirichlet Process Hidden Markov Model. ICMC 2008 - [c25]Matthew D. Hoffman, David M. Blei, Perry R. Cook:
Content-Based Musical Similarity Computation using the Hierarchical Dirichlet Process. ISMIR 2008: 349-354 - [c24]Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing:
Mixed Membership Stochastic Blockmodels. NIPS 2008: 33-40 - [c23]Jordan L. Boyd-Graber, David M. Blei:
Syntactic Topic Models. NIPS 2008: 185-192 - [c22]Indraneel Mukherjee, David M. Blei:
Relative Performance Guarantees for Approximate Inference in Latent Dirichlet Allocation. NIPS 2008: 1129-1136 - [c21]Chong Wang, David M. Blei, David Heckerman:
Continuous Time Dynamic Topic Models. UAI 2008: 579-586 - 2007
- [c20]Jordan L. Boyd-Graber, David M. Blei, Xiaojin Zhu:
A Topic Model for Word Sense Disambiguation. EMNLP-CoNLL 2007: 1024-1033 - [c19]David M. Kaplan, David M. Blei:
A Computational Approach to Style in American Poetry. ICDM 2007: 553-558 - [c18]Miroslav Dudík, David M. Blei, Robert E. Schapire:
Hierarchical maximum entropy density estimation. ICML 2007: 249-256 - [c17]David M. Blei, Jon D. McAuliffe:
Supervised Topic Models. NIPS 2007: 121-128 - [c16]Jonathan D. Chang, Miroslav Dudík, David M. Blei:
PU-BCD: Exponential Family Models for the Coarse- and Fine-Grained All-Words Tasks. SemEval@ACL 2007: 272-276 - [c15]Jordan L. Boyd-Graber, David M. Blei:
PUTOP: Turning Predominant Senses into a Topic Model for Word Sense Disambiguation. SemEval@ACL 2007: 277-281 - [c14]Wei Li, David M. Blei, Andrew McCallum:
Nonparametric Bayes Pachinko Allocation. UAI 2007: 243-250 - [e1]Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Anna Goldenberg, Eric P. Xing, Alice X. Zheng:
Statistical Network Analysis: Models, Issues, and New Directions - ICML 2006 Workshop on Statistical Network Analysis, Pittsburgh, PA, USA, June 29, 2006, Revised Selected Papers. Lecture Notes in Computer Science 4503, Springer 2007, ISBN 978-3-540-73132-0 [contents] - [i1]Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing:
Mixed membership stochastic blockmodels. CoRR abs/0705.4485 (2007) - 2006
- [j4]David M. Blei, K. Franks, Michael I. Jordan, I. Saira Mian:
Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span. BMC Bioinform. 7: 250 (2006) - [j3]Jon D. McAuliffe, David M. Blei, Michael I. Jordan:
Nonparametric empirical Bayes for the Dirichlet process mixture model. Stat. Comput. 16(1): 5-14 (2006) - [c13]Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, Eric P. Xing:
Combining Stochastic Block Models and Mixed Membership for Statistical Network Analysis. SNA@ICML 2006: 57-74 - [c12]David M. Blei, John D. Lafferty:
Dynamic topic models. ICML 2006: 113-120 - [c11]David M. Blei:
Panel Discussion. SNA@ICML 2006: 186-194 - 2005
- [c10]Edoardo M. Airoldi, David M. Blei, Eric P. Xing, Stephen E. Fienberg:
A latent mixed membership model for relational data. LinkKDD 2005: 82-89 - [c9]David M. Blei, John D. Lafferty:
Correlated Topic Models. NIPS 2005: 147-154 - 2004
- [c8]David M. Blei, Michael I. Jordan:
Variational methods for the Dirichlet process. ICML 2004 - [c7]Thomas L. Griffiths, Mark Steyvers, David M. Blei, Joshua B. Tenenbaum:
Integrating Topics and Syntax. NIPS 2004: 537-544 - [c6]Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei:
Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. NIPS 2004: 1385-1392 - 2003
- [j2]David M. Blei, Andrew Y. Ng, Michael I. Jordan:
Latent Dirichlet Allocation. J. Mach. Learn. Res. 3: 993-1022 (2003) - [j1]Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan:
Matching Words and Pictures. J. Mach. Learn. Res. 3: 1107-1135 (2003) - [c5]David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum:
Hierarchical Topic Models and the Nested Chinese Restaurant Process. NIPS 2003: 17-24 - [c4]David M. Blei, Michael I. Jordan:
Modeling annotated data. SIGIR 2003: 127-134 - 2002
- [c3]David M. Blei, J. Andrew Bagnell, Andrew Kachites McCallum:
Learning with Scope, with Application to Information Extraction and Classification. UAI 2002: 53-60 - 2001
- [c2]David M. Blei, Andrew Y. Ng, Michael I. Jordan:
Latent Dirichlet Allocation. NIPS 2001: 601-608 - [c1]David M. Blei, Pedro J. Moreno:
Topic Segmentation with an Aspect Hidden Markov Model. SIGIR 2001: 343-348
Coauthor Index
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