Vikash K. Mansinghka

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 67

Top Topics

Variational Inference

Expected Values

Generative Model

Database Tables

Top Venues

Proc. ACM Program. Lang.

Publications

Mathieu Huot, Matin Ghavami, Alexander K. Lew, Ulrich Schaechtle, Cameron E. Freer, Zane Shelby, Martin C. Rinard, Feras A. Saad, Vikash K. Mansinghka
GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables. Proc. ACM Program. Lang. 8 (PLDI) (2024)
McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard, Vikash K. Mansinghka
Probabilistic Programming with Programmable Variational Inference. CoRR (2024)
McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard, Vikash K. Mansinghka
Probabilistic Programming with Programmable Variational Inference. Proc. ACM Program. Lang. 8 (PLDI) (2024)
Mathieu Huot, Matin Ghavami, Alexander K. Lew, Ulrich Schaechtle, Cameron E. Freer, Zane Shelby, Martin C. Rinard, Feras A. Saad, Vikash K. Mansinghka
GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables. CoRR (2024)
Alexander K. Lew, Mathieu Huot, Sam Staton, Vikash K. Mansinghka
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs. Proc. ACM Program. Lang. 7 (POPL) (2023)
Feras A. Saad, Brian J. Patton, Matthew D. Hoffman, Rif A. Saurous, Vikash K. Mansinghka
Sequential Monte Carlo Learning for Time Series Structure Discovery. CoRR (2023)
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. AISTATS (2023)
Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka, Sam Staton
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs. CoRR (2023)
Nishad Gothoskar, Matin Ghavami, Eric Li, Aidan Curtis, Michael Noseworthy, Karen Chung, Brian Patton, William T. Freeman, Joshua B. Tenenbaum, Mirko Klukas, Vikash K. Mansinghka
Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes. CoRR (2023)
Alexander K. Lew, Matin Ghavamizadeh, Martin C. Rinard, Vikash K. Mansinghka
Probabilistic Programming with Stochastic Probabilities. Proc. ACM Program. Lang. 7 (PLDI) (2023)
Gaurav Arya, Ruben Seyer, Frank Schäfer, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Chris Rackauckas, Kartik Chandra, Moritz Schauer
Differentiating Metropolis-Hastings to Optimize Intractable Densities. CoRR (2023)
Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka
3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics. CoRR (2023)
Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation. ICCV (2023)
Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka, Sam Staton
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs. LICS (2023)
Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell, Vikash K. Mansinghka
SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. AISTATS (2023)
Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought. CoRR (2023)
Alexander K. Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash K. Mansinghka
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs. CoRR (2023)
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. ICRA (2022)
Alexander K. Lew, Mathieu Huot, Sam Staton, Vikash K. Mansinghka
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs. CoRR (2022)
Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. CoRR (2022)
Alexander K. Lew, Marco F. Cusumano-Towner, Vikash K. Mansinghka
Recursive Monte Carlo and Variational Inference with Auxiliary Variables. CoRR (2022)
Feras A. Saad, Marco Cusumano-Towner, Vikash K. Mansinghka
Estimators of Entropy and Information via Inference in Probabilistic Models. CoRR (2022)
Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok, Dan Gutfreund, Joshua B. Tenenbaum, Vikash K. Mansinghka
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind. CoRR (2022)
Alexander K. Lew, Marco Cusumano-Towner, Vikash K. Mansinghka
Recursive Monte Carlo and variational inference with auxiliary variables. UAI (2022)
Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka
Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning. CoRR (2022)
Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. CoRR (2022)
Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Josh Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka
3DP3: 3D Scene Perception via Probabilistic Programming. NeurIPS (2021)
Feras A. Saad, Vikash K. Mansinghka
Hierarchical infinite relational model. UAI (2021)
Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka
Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages. CoRR (2021)
Nishad Gothoskar, Marco Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Joshua B. Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka
3DP3: 3D Scene Perception via Probabilistic Programming. CoRR (2021)
Feras A. Saad, Vikash K. Mansinghka
Hierarchical Infinite Relational Model. CoRR (2021)
Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka
SPPL: probabilistic programming with fast exact symbolic inference. PLDI (2021)
Arwa Alanqary, Gloria Z. Lin, Joie Le, Tan Zhi-Xuan, Vikash K. Mansinghka, Joshua B. Tenenbaum
Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind. CoRR (2021)
Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Joshua B. Tenenbaum, Vikash K. Mansinghka
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents. CoRR (2020)
Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka
Exact Symbolic Inference in Probabilistic Programs via Sum-Product Representations. CoRR (2020)
Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash K. Mansinghka
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. CoRR (2020)
Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka
Optimal approximate sampling from discrete probability distributions. Proc. ACM Program. Lang. 4 (POPL) (2020)
Alexander K. Lew, Marco F. Cusumano-Towner, Benjamin Sherman, Michael Carbin, Vikash K. Mansinghka
Trace types and denotational semantics for sound programmable inference in probabilistic languages. Proc. ACM Program. Lang. 4 (POPL) (2020)
Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka
Optimal Approximate Sampling from Discrete Probability Distributions. CoRR (2020)
Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. CoRR (2020)
Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka
Bayesian synthesis of probabilistic programs for automatic data modeling. Proc. ACM Program. Lang. 3 (POPL) (2019)
Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Vikash K. Mansinghka
Gen: a general-purpose probabilistic programming system with programmable inference. PLDI (2019)
Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling. CoRR (2019)
Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions. AISTATS (2019)
Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions. CoRR (2019)
Vikash K. Mansinghka, Ulrich Schaechtle, Shivam Handa, Alexey Radul, Yutian Chen, Martin Rinard
Probabilistic programming with programmable inference. PLDI (2018)
Marco Cusumano-Towner, Benjamin Bichsel, Timon Gehr, Martin T. Vechev, Vikash K. Mansinghka
Incremental inference for probabilistic programs. PLDI (2018)
Marco F. Cusumano-Towner, Vikash K. Mansinghka
Using probabilistic programs as proposals. CoRR (2018)
Marco F. Cusumano-Towner, Vikash K. Mansinghka
A design proposal for Gen: probabilistic programming with fast custom inference via code generation. MAPL@PLDI (2018)
Feras Saad, Leonardo Casarsa, Vikash K. Mansinghka
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes. CoRR (2017)
Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman
Variational Particle Approximations. J. Mach. Learn. Res. 18 (2017)
Marco F. Cusumano-Towner, Vikash K. Mansinghka
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. CoRR (2017)
Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K. Mansinghka
Probabilistic programs for inferring the goals of autonomous agents. CoRR (2017)
Feras A. Saad, Vikash K. Mansinghka
A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series. CoRR (2017)
Marco F. Cusumano-Towner, Vikash K. Mansinghka
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. NIPS (2017)
Marco F. Cusumano-Towner, Vikash K. Mansinghka
Quantifying the probable approximation error of probabilistic inference programs. CoRR (2016)
Marco F. Cusumano-Towner, Vikash K. Mansinghka
Encapsulating models and approximate inference programs in probabilistic modules. CoRR (2016)
Feras Saad, Vikash K. Mansinghka
A Probabilistic Programming Approach To Probabilistic Data Analysis. NIPS (2016)
Feras Saad, Vikash K. Mansinghka
Detecting Dependencies in High-Dimensional, Sparse Databases Using Probabilistic Programming and Non-parametric Bayes. CoRR (2016)
Feras Saad, Vikash K. Mansinghka
Probabilistic Data Analysis with Probabilistic Programming. CoRR (2016)
Marco F. Cusumano-Towner, Vikash K. Mansinghka
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming. CoRR (2016)
Ulrich Schaechtle, Ben Zinberg, Alexey Radul, Kostas Stathis, Vikash K. Mansinghka
Probabilistic Programming with Gaussian Process Memoization. CoRR (2015)
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML (2015)
Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR (2015)
Vikash K. Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. CoRR (2015)
Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash K. Mansinghka
Picture: A probabilistic programming language for scene perception. CVPR (2015)
Vikash K. Mansinghka, Richard Tibbetts, Jay Baxter, Patrick Shafto, Baxter Eaves
BayesDB: A probabilistic programming system for querying the probable implications of data. CoRR (2015)
Vikash K. Mansinghka, Daniel Selsam, Yura N. Perov
Venture: a higher-order probabilistic programming platform with programmable inference. CoRR (2014)
Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli, Joshua B. Tenenbaum
Inverse Graphics with Probabilistic CAD Models. CoRR (2014)
Vikash K. Mansinghka, Eric Jonas
Building fast Bayesian computing machines out of intentionally stochastic, digital parts. CoRR (2014)
Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. NIPS (2013)
Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures CoRR (2013)
Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. CoRR (2013)
Biplab Deka, Alex A. Birklykke, Henry Duwe, Vikash K. Mansinghka, Rakesh Kumar
Markov chain algorithms: A template for building future robust low power systems. ACSSC (2013)
Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum
Structured Priors for Structure Learning. CoRR (2012)
Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum
Church: a language for generative models CoRR (2012)
Vikash K. Mansinghka, Daniel M. Roy, Eric Jonas, Joshua B. Tenenbaum
Exact and Approximate Sampling by Systematic Stochastic Search. AISTATS (2009)
Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum
Church: a language for generative models. UAI (2008)
Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Joshua B. Tenenbaum
AClass: A simple, online, parallelizable algorithm for probabilistic classification. AISTATS (2007)
Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum
Structured Priors for Structure Learning. UAI (2006)
Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum
Learning annotated hierarchies from relational data. NIPS (2006)