Vikash K. Mansinghka

72 papers A* 8A 8Misc 1Journal 51Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Probabilistic Programming with Vectorized Programmable Inference. Proc. ACM Program. Lang. McCoy R. Becker, Mathieu Huot, George Matheos, Xiaoyan Wang, Karen Chung, Colin Smith, Sam Ritchie, Rif A. Saurous, Alexander K. Lew, Martin C. Rinard, Vikash K. Mansinghka
2025	J	`jnl`	Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality. CoRR Lance Ying, Almog Hillel, Ryan Truong, Vikash K. Mansinghka, Joshua B. Tenenbaum, Tan Zhi-Xuan
2025	J	`jnl`	Self-Steering Language Models. CoRR Gabriel Grand, Joshua B. Tenenbaum, Vikash K. Mansinghka, Alexander K. Lew, Jacob Andreas
2025	J	`jnl`	Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs. Proc. ACM Program. Lang. Maddy Bowers, Alexander K. Lew, Joshua B. Tenenbaum, Armando Solar-Lezama, Vikash K. Mansinghka
2024	J	`jnl`	GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables. CoRR Mathieu Huot, Matin Ghavami, Alexander K. Lew, Ulrich Schaechtle, Cameron E. Freer, Zane Shelby, Martin C. Rinard, Feras A. Saad, Vikash K. Mansinghka
2024	J	`jnl`	GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables. Proc. ACM Program. Lang. Mathieu Huot, Matin Ghavami, Alexander K. Lew, Ulrich Schaechtle, Cameron E. Freer, Zane Shelby, Martin C. Rinard, Feras A. Saad, Vikash K. Mansinghka
2024	J	`jnl`	Probabilistic Programming with Programmable Variational Inference. CoRR McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard, Vikash K. Mansinghka
2024	J	`jnl`	Probabilistic Programming with Programmable Variational Inference. Proc. ACM Program. Lang. McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard, Vikash K. Mansinghka
2023	J	`jnl`	3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics. CoRR Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka
2023	A*	`conf`	3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation. ICCV Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka
2023	J	`jnl`	ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs. Proc. ACM Program. Lang. Alexander K. Lew, Mathieu Huot, Sam Staton, Vikash K. Mansinghka
2023	J	`jnl`	Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes. CoRR 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
2023	J	`jnl`	Differentiating Metropolis-Hastings to Optimize Intractable Densities. CoRR Gaurav Arya, Ruben Seyer, Frank Schäfer, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Chris Rackauckas, Kartik Chandra, Moritz Schauer
2023	J	`jnl`	From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought. CoRR Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum
2023	A	`conf`	ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. AISTATS Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous
2023	J	`jnl`	Probabilistic Programming with Stochastic Probabilities. Proc. ACM Program. Lang. Alexander K. Lew, Matin Ghavamizadeh, Martin C. Rinard, Vikash K. Mansinghka
2023	A	`conf`	SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. AISTATS Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell, Vikash K. Mansinghka
2023	J	`jnl`	Sequential Monte Carlo Learning for Time Series Structure Discovery. CoRR Feras A. Saad, Brian J. Patton, Matthew D. Hoffman, Rif A. Saurous, Vikash K. Mansinghka
2023	J	`jnl`	Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs. CoRR Alexander K. Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash K. Mansinghka
2023	A*	`conf`	ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs. LICS Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka, Sam Staton
2023	J	`jnl`	ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs. CoRR Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka, Sam Staton
2022	J	`jnl`	ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs. CoRR Alexander K. Lew, Mathieu Huot, Sam Staton, Vikash K. Mansinghka
2022	J	`jnl`	Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning. CoRR Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka
2022	A*	`conf`	DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. ICRA Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George
2022	J	`jnl`	DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. CoRR Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George
2022	J	`jnl`	Estimators of Entropy and Information via Inference in Probabilistic Models. CoRR Feras A. Saad, Marco F. Cusumano-Towner, Vikash K. Mansinghka
2022	J	`jnl`	ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. CoRR Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous
2022	J	`jnl`	Recursive Monte Carlo and Variational Inference with Auxiliary Variables. CoRR Alexander K. Lew, Marco F. Cusumano-Towner, Vikash K. Mansinghka
2022	A	`conf`	Recursive Monte Carlo and variational inference with auxiliary variables. UAI Alexander K. Lew, Marco F. Cusumano-Towner, Vikash K. Mansinghka
2022	J	`jnl`	Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind. CoRR Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok, Dan Gutfreund, Joshua B. Tenenbaum, Vikash K. Mansinghka
2021	A*	`conf`	3DP3: 3D Scene Perception via Probabilistic Programming. NeurIPS Nishad Gothoskar, Marco F. Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Josh Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka
2021	J	`jnl`	3DP3: 3D Scene Perception via Probabilistic Programming. CoRR Nishad Gothoskar, Marco F. Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Joshua B. Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka
2021	J	`jnl`	Hierarchical Infinite Relational Model. CoRR Feras A. Saad, Vikash K. Mansinghka
2021	A	`conf`	Hierarchical infinite relational model. UAI Feras A. Saad, Vikash K. Mansinghka
2021	J	`jnl`	Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind. CoRR Arwa Alanqary, Gloria Z. Lin, Joie Le, Tan Zhi-Xuan, Vikash K. Mansinghka, Joshua B. Tenenbaum
2021	A*	`conf`	SPPL: probabilistic programming with fast exact symbolic inference. PLDI Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka
2021	J	`jnl`	Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages. CoRR Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka
2020	J	`jnl`	Exact Symbolic Inference in Probabilistic Programs via Sum-Product Representations. CoRR Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka
2020	J	`jnl`	Online Bayesian Goal Inference for Boundedly-Rational Planning Agents. CoRR Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Joshua B. Tenenbaum, Vikash K. Mansinghka
2020	J	`jnl`	Optimal Approximate Sampling from Discrete Probability Distributions. CoRR Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka
2020	J	`jnl`	Optimal approximate sampling from discrete probability distributions. Proc. ACM Program. Lang. Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka
2020	J	`jnl`	PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. CoRR Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash K. Mansinghka
2020	J	`jnl`	The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. CoRR Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka
2020	J	`jnl`	Trace types and denotational semantics for sound programmable inference in probabilistic languages. Proc. ACM Program. Lang. Alexander K. Lew, Marco F. Cusumano-Towner, Benjamin Sherman, Michael Carbin, Vikash K. Mansinghka
2019	A	`conf`	A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions. AISTATS Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka
2019	J	`jnl`	A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions. CoRR Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka
2019	J	`jnl`	Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling. CoRR Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka
2019	J	`jnl`	Bayesian synthesis of probabilistic programs for automatic data modeling. Proc. ACM Program. Lang. Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka
2019	A*	`conf`	Gen: a general-purpose probabilistic programming system with programmable inference. PLDI Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Vikash K. Mansinghka
2018	—	`conf`	A design proposal for Gen: probabilistic programming with fast custom inference via code generation. MAPL@PLDI Marco F. Cusumano-Towner, Vikash K. Mansinghka
2018	A*	`conf`	Incremental inference for probabilistic programs. PLDI Marco F. Cusumano-Towner, Benjamin Bichsel, Timon Gehr, Martin T. Vechev, Vikash K. Mansinghka
2018	A*	`conf`	Probabilistic programming with programmable inference. PLDI Vikash K. Mansinghka, Ulrich Schaechtle, Shivam Handa, Alexey Radul, Yutian Chen, Martin C. Rinard
2018	J	`jnl`	Using probabilistic programs as proposals. CoRR Marco F. Cusumano-Towner, Vikash K. Mansinghka
2017	J	`jnl`	A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series. CoRR Feras A. Saad, Vikash K. Mansinghka
2017	—	`conf`	AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. NIPS Marco F. Cusumano-Towner, Vikash K. Mansinghka
2017	J	`jnl`	AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. CoRR Marco F. Cusumano-Towner, Vikash K. Mansinghka
2017	J	`jnl`	Probabilistic programs for inferring the goals of autonomous agents. CoRR Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K. Mansinghka
2017	J	`jnl`	Variational Particle Approximations. J. Mach. Learn. Res. Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman
2016	—	`conf`	A Probabilistic Programming Approach To Probabilistic Data Analysis. NIPS Feras Saad, Vikash K. Mansinghka
2016	J	`jnl`	Encapsulating models and approximate inference programs in probabilistic modules. CoRR Marco F. Cusumano-Towner, Vikash K. Mansinghka
2016	J	`jnl`	Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming. CoRR Marco F. Cusumano-Towner, Vikash K. Mansinghka
2016	J	`jnl`	Quantifying the probable approximation error of probabilistic inference programs. CoRR Marco F. Cusumano-Towner, Vikash K. Mansinghka
2015	J	`jnl`	JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka
2015	J	`jnl`	Probabilistic Programming with Gaussian Process Memoization. CoRR Ulrich Schaechtle, Ben Zinberg, Alexey Radul, Kostas Stathis, Vikash K. Mansinghka
2014	J	`jnl`	Inverse Graphics with Probabilistic CAD Models. CoRR Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli, Joshua B. Tenenbaum
2013	—	`conf`	Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. NIPS Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum
2013	J	`jnl`	Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. CoRR Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum
2013	J	`jnl`	ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures CoRR Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka
2013	Misc	`conf`	Markov chain algorithms: A template for building future robust low power systems. ACSSC Biplab Deka, Alex A. Birklykke, Henry Duwe, Vikash K. Mansinghka, Rakesh Kumar
2008	A	`conf`	Church: a language for generative models. UAI Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum
2007	A	`conf`	AClass: A simple, online, parallelizable algorithm for probabilistic classification. AISTATS Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Joshua B. Tenenbaum
2006	A	`conf`	Structured Priors for Structure Learning. UAI Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum