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A Simulation-Based Test of Identifiability for Bayesian Causal Inference

Feb 23, 2021
Sam Witty, David Jensen, Vikash Mansinghka

* 16 pages, 5 figures 

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Causal Inference using Gaussian Processes with Structured Latent Confounders

Jul 14, 2020
Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka

* to be published at ICML2020 

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Deep Involutive Generative Models for Neural MCMC

Jul 02, 2020
Span Spanbauer, Cameron Freer, Vikash Mansinghka

* 13 pages, 6 figures. Revised discussion of the Jacobian determinant factor in the acceptance ratio 

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Bayesian causal inference via probabilistic program synthesis

Oct 30, 2019
Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka

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Real-time Approximate Bayesian Computation for Scene Understanding

May 22, 2019
Javier Felip, Nilesh Ahuja, David Gómez-Gutiérrez, Omesh Tickoo, Vikash Mansinghka

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Time Series Structure Discovery via Probabilistic Program Synthesis

May 22, 2017
Ulrich Schaechtle, Feras Saad, Alexey Radul, Vikash Mansinghka

* The first two authors contributed equally to this work 

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Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes

Apr 04, 2017
Feras Saad, Leonardo Casarsa, Vikash Mansinghka

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Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes

Mar 27, 2017
Feras Saad, Vikash Mansinghka

* Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:632-641, 2017 

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Probabilistic Data Analysis with Probabilistic Programming

Aug 18, 2016
Feras Saad, Vikash Mansinghka

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BayesDB: A probabilistic programming system for querying the probable implications of data

Dec 15, 2015
Vikash Mansinghka, Richard Tibbetts, Jay Baxter, Pat Shafto, Baxter Eaves

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Variational Particle Approximations

Dec 06, 2015
Ardavan Saeedi, Tejas D Kulkarni, Vikash Mansinghka, Samuel Gershman

* First two authors contributed equally to this work 

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CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data

Dec 03, 2015
Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum

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A New Approach to Probabilistic Programming Inference

Jul 09, 2015
Frank Wood, Jan Willem van de Meent, Vikash Mansinghka

* Updated version of the 2014 AISTATS paper (to reflect changes in new language syntax). 10 pages, 3 figures. Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, JMLR Workshop and Conference Proceedings, Vol 33, 2014 

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Automatic Inference for Inverting Software Simulators via Probabilistic Programming

May 31, 2015
Ardavan Saeedi, Vlad Firoiu, Vikash Mansinghka

* ICML 2014 AutoML Workshop 

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Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs

Mar 09, 2015
Yutian Chen, Vikash Mansinghka, Zoubin Ghahramani

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Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Feb 09, 2015
Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank Wood

* 9 pages, 2 figures 

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Church: a language for generative models

Jul 15, 2014
Noah Goodman, Vikash Mansinghka, Daniel M. Roy, Keith Bonawitz, Joshua B. Tenenbaum

* Minor revisions. Fixed errors in author list 

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Venture: a higher-order probabilistic programming platform with programmable inference

Apr 01, 2014
Vikash Mansinghka, Daniel Selsam, Yura Perov

* 78 pages 

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Building fast Bayesian computing machines out of intentionally stochastic, digital parts

Feb 20, 2014
Vikash Mansinghka, Eric Jonas

* 6 figures 

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Structured Priors for Structure Learning

Jun 27, 2012
Vikash Mansinghka, Charles Kemp, Thomas Griffiths, Joshua Tenenbaum

* Appears in Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI2006) 

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