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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
- 2023
[i88]Yixin Wang, David M. Blei, John P. Cunningham:
Posterior Collapse and Latent Variable Non-identifiability. CoRR abs/2301.00537 (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