default search action
Google
Google Scholar
Semantic Scholar
Internet Archive Scholar
CiteSeerX
ORCIDDavid M. Blei
Person information
- affiliation: Columbia University, New York City, USA
- award: ACM Prize in Computing, 2013
Refine list
refinements active!
zoomed in on ?? of ?? records
view refined list in
export refined list as
showing all ?? records
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
manage site settings
To protect your privacy, all features that rely on external API calls from your browser are turned off by default. You need to opt-in for them to become active. All settings here will be stored as cookies with your web browser. For more information see our F.A.Q.
Unpaywalled article links
Add open access links from 
to the list of external document links (if available).
Privacy notice: By enabling the option above, your browser will contact the API of unpaywall.org to load hyperlinks to open access articles. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Unpaywall privacy policy.
Archived links via Wayback Machine
For web page which are no longer available, try to retrieve content from the 
of the Internet Archive (if available).
Privacy notice: By enabling the option above, your browser will contact the API of archive.org to check for archived content of web pages that are no longer available. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Internet Archive privacy policy.
Reference lists
Add a list of references from 
, 
, and 
to record detail pages.
load references from crossref.org and opencitations.net
Privacy notice: By enabling the option above, your browser will contact the APIs of crossref.org, opencitations.net, and semanticscholar.org to load article reference information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the Crossref privacy policy and the OpenCitations privacy policy, as well as the AI2 Privacy Policy covering Semantic Scholar.
Citation data
Add a list of citing articles from and to record detail pages.
load citations from opencitations.net
Privacy notice: By enabling the option above, your browser will contact the API of opencitations.net and semanticscholar.org to load citation information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the OpenCitations privacy policy as well as the AI2 Privacy Policy covering Semantic Scholar.
OpenAlex data
Load additional information about publications from 
.
Privacy notice: By enabling the option above, your browser will contact the API of openalex.org to load additional information. Although we do not have any reason to believe that your call will be tracked, we do not have any control over how the remote server uses your data. So please proceed with care and consider checking the information given by OpenAlex.
last updated on 2024-11-11 22:24 CET by the dblp team
all metadata released as open data under CC0 1.0 license
see also: Terms of Use | Privacy Policy | Imprint
dblp was originally created in 1993 at:
since 2018, dblp has been operated and maintained by:
the dblp computer science bibliography is funded and supported by:
