Vikash Mansinghka

According to our database, Vikash Mansinghka authored at least 85 papers between 2006 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables.

Mathieu Huot, Matin Ghavami, Alexander K. Lew, Ulrich Schaechtle, Cameron E. Freer, Zane Shelby, Martin C. Rinard, Feras A. Saad, Vikash K. Mansinghka

Proc. ACM Program. Lang., 2024

Probabilistic Programming with Programmable Variational Inference.

McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard, Vikash K. Mansinghka

Proc. ACM Program. Lang., 2024

Understanding Epistemic Language with a Bayesian Theory of Mind.

Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum

CoRR, 2024

Building Machines that Learn and Think with People.

Katherine M. Collins, Ilia Sucholutsky, Umang Bhatt, Kartik Chandra, Lionel Wong, Mina Lee, Cedegao E. Zhang, Tan Zhi-Xuan, Mark K. Ho, Vikash Mansinghka, Adrian Weller, Joshua B. Tenenbaum, Thomas L. Griffiths

CoRR, 2024

Infinite Ends from Finite Samples: Open-Ended Goal Inference as Top-Down Bayesian Filtering of Bottom-Up Proposals.

Tan Zhi-Xuan, Gloria Kang, Vikash Mansinghka, Joshua B. Tenenbaum

CoRR, 2024

Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness.

Aidan Curtis, George Matheos, Nishad Gothoskar, Vikash Mansinghka, Joshua B. Tenenbaum, Tomás Lozano-Pérez, Leslie Pack Kaelbling

CoRR, 2024

Grounding Language about Belief in a Bayesian Theory-of-Mind.

Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum

CoRR, 2024

Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning.

Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, Joshua B. Tenenbaum

Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems, 2024

2023

ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs.

Alexander K. Lew, Mathieu Huot, Sam Staton, Vikash K. Mansinghka

Proc. ACM Program. Lang., January, 2023

Probabilistic Programming with Stochastic Probabilities.

Alexander K. Lew, Matin Ghavamizadeh, Martin C. Rinard, Vikash K. Mansinghka

Proc. ACM Program. Lang., 2023

Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes.

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

CoRR, 2023

Inferring the Goals of Communicating Agents from Actions and Instructions.

Lance Ying, Tan Zhi-Xuan, Vikash Mansinghka, Joshua B. Tenenbaum

CoRR, 2023

From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought.

Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum

CoRR, 2023

Differentiating Metropolis-Hastings to Optimize Intractable Densities.

Gaurav Arya, Ruben Seyer, Frank Schäfer, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Chris Rackauckas, Kartik Chandra, Moritz Schauer

CoRR, 2023

Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs.

Alexander K. Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash K. Mansinghka

CoRR, 2023

3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics.

Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka

CoRR, 2023

ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs.

Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka, Sam Staton

Proceedings of the 38th Annual ACM/IEEE Symposium on Logic in Computer Science, 2023

Sequential Monte Carlo Learning for Time Series Structure Discovery.

Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka

Proceedings of the International Conference on Machine Learning, 2023

3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation.

Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka

Proceedings of the IEEE/CVF International Conference on Computer Vision, 2023

Language Models as Informative Goal Priors in a Bayesian Theory of Mind.

Tan Zhi-Xuan, Paul Stefan Lunis, Nathalie Fernandez Echeverri, Vikash Mansinghka, Josh Tenenbaum

Proceedings of the 45th Annual Meeting of the Cognitive Science Society, 2023

SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals.

Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell, Vikash K. Mansinghka

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images.

Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning.

Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka

CoRR, 2022

Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind.

Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok, Dan Gutfreund, Joshua B. Tenenbaum, Vikash K. Mansinghka

CoRR, 2022

Recursive Monte Carlo and variational inference with auxiliary variables.

Alexander K. Lew, Marco F. Cusumano-Towner, Vikash K. Mansinghka

Proceedings of the Uncertainty in Artificial Intelligence, 2022

DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model.

Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George

Proceedings of the 2022 International Conference on Robotics and Automation, 2022

Estimators of Entropy and Information via Inference in Probabilistic Models.

Feras Saad, Marco F. Cusumano-Towner, Vikash Mansinghka

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages.

Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka

CoRR, 2021

From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence.

Nicholas Roy, Ingmar Posner, Tim D. Barfoot, Philippe Beaudoin, Yoshua Bengio, Jeannette Bohg, Oliver Brock, Isabelle Depatie, Dieter Fox, Daniel E. Koditschek, Tomás Lozano-Pérez, Vikash Mansinghka, Christopher J. Pal, Blake A. Richards, Dorsa Sadigh, Stefan Schaal, Gaurav S. Sukhatme, Denis Thérien, Marc Toussaint, Michiel van de Panne

CoRR, 2021

A Simulation-Based Test of Identifiability for Bayesian Causal Inference.

Sam Witty, David D. Jensen, Vikash Mansinghka

CoRR, 2021

Hierarchical infinite relational model.

Feras A. Saad, Vikash K. Mansinghka

Proceedings of the Thirty-Seventh Conference on Uncertainty in Artificial Intelligence, 2021

SPPL: probabilistic programming with fast exact symbolic inference.

Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka

Proceedings of the PLDI '21: 42nd ACM SIGPLAN International Conference on Programming Language Design and Implementation, 2021

3DP3: 3D Scene Perception via Probabilistic Programming.

Nishad Gothoskar, Marco F. Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Josh Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind.

Arwa Alanqary, Gloria Z. Lin, Joie Le, Tan Zhi-Xuan, Vikash Mansinghka, Josh Tenenbaum

Proceedings of the 43rd Annual Meeting of the Cognitive Science Society, 2021

PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming.

Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka

Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021

2020

Optimal approximate sampling from discrete probability distributions.

Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka

Proc. ACM Program. Lang., 2020

Trace types and denotational semantics for sound programmable inference in probabilistic languages.

Alexander K. Lew, Marco F. Cusumano-Towner, Benjamin Sherman, Michael Carbin, Vikash K. Mansinghka

Proc. ACM Program. Lang., 2020

Exact Symbolic Inference in Probabilistic Programs via Sum-Product Representations.

Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka

CoRR, 2020

Deep Involutive Generative Models for Neural MCMC.

Span Spanbauer, Cameron E. Freer, Vikash Mansinghka

CoRR, 2020

Online Bayesian Goal Inference for Boundedly Rational Planning Agents.

Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Josh Tenenbaum, Vikash Mansinghka

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Causal Inference using Gaussian Processes with Structured Latent Confounders.

Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka

Proceedings of the 37th International Conference on Machine Learning, 2020

Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought.

Alexander K. Lew, Michael Henry Tessler, Vikash Mansinghka, Josh Tenenbaum

Proceedings of the 42th Annual Meeting of the Cognitive Science Society, 2020

The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions.

Feras Saad, Cameron E. Freer, Martin C. Rinard, Vikash Mansinghka

Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics, 2020

2019

Bayesian synthesis of probabilistic programs for automatic data modeling.

Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka

Proc. ACM Program. Lang., 2019

Bayesian causal inference via probabilistic program synthesis.

Sam Witty, Alexander K. Lew, David D. Jensen, Vikash Mansinghka

CoRR, 2019

Compositional Inference Metaprogramming with Convergence Guarantees.

Shivam Handa, Vikash Mansinghka, Martin C. Rinard

CoRR, 2019

Real-time Approximate Bayesian Computation for Scene Understanding.

Javier Felip, Nilesh A. Ahuja, David Gómez-Gutiérrez, Omesh Tickoo, Vikash Mansinghka

CoRR, 2019

Usability of Probabilistic Programming Languages.

Alan F. Blackwell, Luke Church, Martin Erwig, James Geddes, Andy Gordon, Maria I. Gorinova, Atilim Gunes Baydin, Bradley Gram-Hansen, Tobias Kohn, Neil D. Lawrence, Vikash Mansinghka, Brooks Paige, Tomas Petricek, Diana Robinson, Advait Sarkar, Oliver Strickson

Proceedings of the 30th Annual Workshop of the Psychology of Programming Interest Group, 2019

Gen: a general-purpose probabilistic programming system with programmable inference.

Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Vikash K. Mansinghka

Proceedings of the 40th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2019

A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions.

Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka

Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics, 2019

2018

Using probabilistic programs as proposals.

Marco F. Cusumano-Towner, Vikash K. Mansinghka

CoRR, 2018

Probabilistic programming with programmable inference.

Vikash K. Mansinghka, Ulrich Schaechtle, Shivam Handa, Alexey Radul, Yutian Chen, Martin C. Rinard

Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2018

Incremental inference for probabilistic programs.

Marco F. Cusumano-Towner, Benjamin Bichsel, Timon Gehr, Martin T. Vechev, Vikash K. Mansinghka

Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2018

A design proposal for Gen: probabilistic programming with fast custom inference via code generation.

Marco F. Cusumano-Towner, Vikash K. Mansinghka

Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine Learning and Programming Languages, 2018

Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series.

Feras Saad, Vikash Mansinghka

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2018

2017

Variational Particle Approximations.

Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman

J. Mach. Learn. Res., 2017

A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series.

Feras A. Saad, Vikash K. Mansinghka

CoRR, 2017

Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes.

Feras Saad, Leonardo Casarsa, Vikash Mansinghka

CoRR, 2017

Probabilistic programs for inferring the goals of autonomous agents.

Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K. Mansinghka

CoRR, 2017

AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms.

Marco F. Cusumano-Towner, Vikash K. Mansinghka

Proceedings of the Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, 2017

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

Feras Saad, Vikash Mansinghka

Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, 2017

2016

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

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

J. Mach. Learn. Res., 2016

Detecting Dependencies in High-Dimensional, Sparse Databases Using Probabilistic Programming and Non-parametric Bayes.

Feras Saad, Vikash Mansinghka

CoRR, 2016

Probabilistic Data Analysis with Probabilistic Programming.

Feras Saad, Vikash Mansinghka

CoRR, 2016

Encapsulating models and approximate inference programs in probabilistic modules.

Marco F. Cusumano-Towner, Vikash K. Mansinghka

CoRR, 2016

Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming.

Marco F. Cusumano-Towner, Vikash K. Mansinghka

CoRR, 2016

Quantifying the probable approximation error of probabilistic inference programs.

Marco F. Cusumano-Towner, Vikash K. Mansinghka

CoRR, 2016

A Probabilistic Programming Approach To Probabilistic Data Analysis.

Feras Saad, Vikash K. Mansinghka

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

2015

Probabilistic Programming with Gaussian Process Memoization.

Ulrich Schaechtle, Ben Zinberg, Alexey Radul, Kostas Stathis, Vikash K. Mansinghka

CoRR, 2015

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

Vikash Mansinghka, Richard Tibbetts, Jay Baxter, Patrick Shafto, Baxter Eaves

CoRR, 2015

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes.

Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka

Proceedings of the 32nd International Conference on Machine Learning, 2015

Picture: A probabilistic programming language for scene perception.

Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash Mansinghka

Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015

Particle Gibbs with Ancestor Sampling for Probabilistic Programs.

Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood

Proceedings of the Eighteenth International Conference on Artificial Intelligence and Statistics, 2015

2014

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

Vikash Mansinghka, Daniel Selsam, Yura N. Perov

CoRR, 2014

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

Vikash Mansinghka, Eric Jonas

CoRR, 2014

Inverse Graphics with Probabilistic CAD Models.

Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli, Joshua B. Tenenbaum

CoRR, 2014

A New Approach to Probabilistic Programming Inference.

Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka

Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, 2014

2013

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures

Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka

CoRR, 2013

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs.

Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum

Proceedings of the Advances in Neural Information Processing Systems 26: 27th Annual Conference on Neural Information Processing Systems 2013. Proceedings of a meeting held December 5-8, 2013

Markov chain algorithms: A template for building future robust low power systems.

Biplab Deka, Alex A. Birklykke, Henry Duwe, Vikash K. Mansinghka, Rakesh Kumar

Proceedings of the 2013 Asilomar Conference on Signals, 2013

2009

Exact and Approximate Sampling by Systematic Stochastic Search.

Vikash Mansinghka, Daniel M. Roy, Eric Jonas, Joshua B. Tenenbaum

Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics, 2009

2008

Church: a language for generative models.

Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum

Proceedings of the UAI 2008, 2008

2007

AClass: A simple, online, parallelizable algorithm for probabilistic classification.

Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Joshua B. Tenenbaum

Proceedings of the Eleventh International Conference on Artificial Intelligence and Statistics, 2007

2006

Structured Priors for Structure Learning.

Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum

Proceedings of the UAI '06, 2006

Learning annotated hierarchies from relational data.

Daniel M. Roy, Charles Kemp, Vikash Mansinghka, Joshua B. Tenenbaum

Proceedings of the Advances in Neural Information Processing Systems 19, 2006