Learning annotated hierarchies from relational data. - Daniel M. Roy 001; Charles Kemp; Vikash Mansinghka 001; Joshua B. Tenenbaum (NIPS, 2006)

Structured Priors for Structure Learning. - Vikash K. Mansinghka; Charles Kemp; Thomas L. Griffiths 001; Joshua B. Tenenbaum (UAI, 2006)

AClass: A simple, online, parallelizable algorithm for probabilistic classification. - Vikash K. Mansinghka; Daniel M. Roy 001; Ryan Rifkin; Joshua B. Tenenbaum (AISTATS, 2007)

Church: a language for generative models. - Noah D. Goodman; Vikash K. Mansinghka; Daniel M. Roy 001; Kallista A. Bonawitz; Joshua B. Tenenbaum (UAI, 2008)

Exact and Approximate Sampling by Systematic Stochastic Search. - Vikash Mansinghka 001; Daniel M. Roy 001; Eric Jonas; Joshua B. Tenenbaum (AISTATS, 2009)

Markov chain algorithms: A template for building future robust low power systems. - Biplab Deka; Alex A. Birklykke; Henry Duwe; Vikash K. Mansinghka; Rakesh Kumar 002 (ACSSC, 2013)

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. - Vikash K. Mansinghka; Tejas D. Kulkarni; Yura N. Perov; Joshua B. Tenenbaum (NIPS, 2013)

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures - Dan Lovell; Jonathan Malmaud; Ryan P. Adams; Vikash K. Mansinghka (CoRR, 2013)

A New Approach to Probabilistic Programming Inference. - Frank D. Wood; Jan-Willem van de Meent; Vikash Mansinghka 001 (AISTATS, 2014)

Inverse Graphics with Probabilistic CAD Models. - Tejas D. Kulkarni; Vikash K. Mansinghka; Pushmeet Kohli; Joshua B. Tenenbaum (CoRR, 2014)

Building fast Bayesian computing machines out of intentionally stochastic, digital parts. - Vikash Mansinghka 001; Eric Jonas (CoRR, 2014)

Venture: a higher-order probabilistic programming platform with programmable inference. - Vikash Mansinghka 001; Daniel Selsam; Yura N. Perov (CoRR, 2014)

Particle Gibbs with Ancestor Sampling for Probabilistic Programs. - Jan-Willem van de Meent; Hongseok Yang; Vikash Mansinghka 001; Frank D. Wood (AISTATS, 2015)

Picture: A probabilistic programming language for scene perception. - Tejas D. Kulkarni; Pushmeet Kohli; Joshua B. Tenenbaum; Vikash Mansinghka 001 (CVPR, 2015)

JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. - Jonathan H. Huggins; Karthik Narasimhan; Ardavan Saeedi; Vikash Mansinghka 001 (ICML, 2015)

BayesDB: A probabilistic programming system for querying the probable implications of data. - Vikash Mansinghka 001; Richard Tibbetts; Jay Baxter; Patrick Shafto; Baxter Eaves (CoRR, 2015)

Probabilistic Programming with Gaussian Process Memoization. - Ulrich Schaechtle; Ben Zinberg; Alexey Radul; Kostas Stathis; Vikash K. Mansinghka (CoRR, 2015)

A Probabilistic Programming Approach To Probabilistic Data Analysis. - Feras Saad; Vikash K. Mansinghka (NIPS, 2016)

Quantifying the probable approximation error of probabilistic inference programs. - 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)

Encapsulating models and approximate inference programs in probabilistic modules. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (CoRR, 2016)

Probabilistic Data Analysis with Probabilistic Programming. - Feras Saad; Vikash Mansinghka 001 (CoRR, 2016)

Detecting Dependencies in High-Dimensional, Sparse Databases Using Probabilistic Programming and Non-parametric Bayes. - Feras Saad; Vikash Mansinghka 001 (CoRR, 2016)

CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. - Vikash Mansinghka 001; Patrick Shafto; Eric Jonas; Cap Petschulat; Max Gasner; Joshua B. Tenenbaum (J. Mach. Learn. Res., 2016)

Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes. - Feras Saad; Vikash Mansinghka 001 (AISTATS, 2017)

AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (NIPS, 2017)

Probabilistic programs for inferring the goals of autonomous agents. - Marco F. Cusumano-Towner; Alexey Radul; David Wingate; Vikash K. Mansinghka (CoRR, 2017)

Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes. - Feras Saad; Leonardo Casarsa; Vikash Mansinghka 001 (CoRR, 2017)

A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series. - Feras A. Saad; Vikash K. Mansinghka (CoRR, 2017)

Variational Particle Approximations. - Ardavan Saeedi; Tejas D. Kulkarni; Vikash K. Mansinghka; Samuel J. Gershman (J. Mach. Learn. Res., 2017)

Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series. - Feras Saad; Vikash Mansinghka 001 (AISTATS, 2018)

A design proposal for Gen: probabilistic programming with fast custom inference via code generation. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (MAPL@PLDI, 2018)

Incremental inference for probabilistic programs. - Marco F. Cusumano-Towner; Benjamin Bichsel; Timon Gehr; Martin T. Vechev; Vikash K. Mansinghka (PLDI, 2018)

Probabilistic programming with programmable inference. - Vikash K. Mansinghka; Ulrich Schaechtle; Shivam Handa; Alexey Radul; Yutian Chen; Martin C. Rinard (PLDI, 2018)

Using probabilistic programs as proposals. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (CoRR, 2018)

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 (AISTATS, 2019)

Gen: a general-purpose probabilistic programming system with programmable inference. - Marco F. Cusumano-Towner; Feras A. Saad; Alexander K. Lew; Vikash K. Mansinghka (PLDI, 2019)

Usability of Probabilistic Programming Languages. - Alan F. Blackwell; Luke Church; Martin Erwig; James Geddes; Andy Gordon 001; Maria I. Gorinova 001; Atilim Gunes Baydin; Bradley Gram-Hansen; Tobias Kohn; Neil D. Lawrence; Vikash Mansinghka 001; Brooks Paige; et al. (PPIG, 2019)

Real-time Approximate Bayesian Computation for Scene Understanding. - Javier Felip; Nilesh A. Ahuja; David Gómez-Gutiérrez; Omesh Tickoo; Vikash Mansinghka 001 (CoRR, 2019)

Compositional Inference Metaprogramming with Convergence Guarantees. - Shivam Handa; Vikash Mansinghka 001; Martin C. Rinard (CoRR, 2019)

Bayesian causal inference via probabilistic program synthesis. - Sam Witty; Alexander K. Lew; David D. Jensen; Vikash Mansinghka 001 (CoRR, 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)

The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. - Feras Saad; Cameron E. Freer; Martin C. Rinard; Vikash Mansinghka 001 (AISTATS, 2020)

Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought. - Alexander K. Lew; Michael Henry Tessler; Vikash Mansinghka 001; Josh Tenenbaum 001 (CogSci, 2020)

Causal Inference using Gaussian Processes with Structured Latent Confounders. - Sam Witty; Kenta Takatsu; David D. Jensen; Vikash Mansinghka 001 (ICML, 2020)

Online Bayesian Goal Inference for Boundedly Rational Planning Agents. - Tan Zhi-Xuan; Jordyn L. Mann; Tom Silver; Josh Tenenbaum 001; Vikash Mansinghka 001 (NeurIPS, 2020)

Deep Involutive Generative Models for Neural MCMC. - Span Spanbauer; Cameron E. Freer; Vikash Mansinghka 001 (CoRR, 2020)

Exact Symbolic Inference in Probabilistic Programs via Sum-Product Representations. - Feras A. Saad; Martin C. Rinard; Vikash K. Mansinghka (CoRR, 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)

Optimal approximate sampling from discrete probability distributions. - Feras A. Saad; Cameron E. Freer; Martin C. Rinard; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2020)

PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. - Alexander K. Lew; Monica Agrawal; David A. Sontag; Vikash Mansinghka 001 (AISTATS, 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 001; Josh Tenenbaum 001 (CogSci, 2021)

3DP3: 3D Scene Perception via Probabilistic Programming. - Nishad Gothoskar; Marco F. Cusumano-Towner; Ben Zinberg; Matin Ghavamizadeh; Falk Pollok; Austin Garrett; Josh Tenenbaum 001; Dan Gutfreund; Vikash K. Mansinghka (NeurIPS, 2021)

SPPL: probabilistic programming with fast exact symbolic inference. - Feras A. Saad; Martin C. Rinard; Vikash K. Mansinghka (PLDI, 2021)

Hierarchical infinite relational model. - Feras A. Saad; Vikash K. Mansinghka (UAI, 2021)

A Simulation-Based Test of Identifiability for Bayesian Causal Inference. - Sam Witty; David D. Jensen; Vikash Mansinghka 001 (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 001; et al. (CoRR, 2021)

Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages. - Alexander K. Lew; Mathieu Huot; Vikash K. Mansinghka (CoRR, 2021)

Estimators of Entropy and Information via Inference in Probabilistic Models. - Feras Saad; Marco F. Cusumano-Towner; Vikash Mansinghka 001 (AISTATS, 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 (ICRA, 2022)

Recursive Monte Carlo and variational inference with auxiliary variables. - Alexander K. Lew; Marco F. Cusumano-Towner; Vikash K. Mansinghka (UAI, 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)

Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning. - Tan Zhi-Xuan; Joshua B. Tenenbaum; Vikash K. Mansinghka (CoRR, 2022)

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 (AISTATS, 2023)

SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. - Alexander K. Lew; George Matheos; Tan Zhi-Xuan; Matin Ghavamizadeh; Nishad Gothoskar; Stuart Russell 001; Vikash K. Mansinghka (AISTATS, 2023)

Language Models as Informative Goal Priors in a Bayesian Theory of Mind. - Tan Zhi-Xuan; Paul Stefan Lunis; Nathalie Fernandez Echeverri; Vikash Mansinghka 001; Josh Tenenbaum 001 (CogSci, 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 (ICCV, 2023)

Sequential Monte Carlo Learning for Time Series Structure Discovery. - Feras Saad; Brian Patton; Matthew Douglas Hoffman; Rif A. Saurous; Vikash Mansinghka 001 (ICML, 2023)

ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs. - Mathieu Huot; Alexander K. Lew; Vikash K. Mansinghka; Sam Staton (LICS, 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)

Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs. - Alexander K. Lew; Tan Zhi-Xuan; Gabriel Grand; Vikash K. Mansinghka (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)

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)

Inferring the Goals of Communicating Agents from Actions and Instructions. - Lance Ying; Tan Zhi-Xuan; Vikash Mansinghka 001; Joshua B. Tenenbaum (CoRR, 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)

Probabilistic Programming with Stochastic Probabilities. - Alexander K. Lew; Matin Ghavamizadeh; Martin C. Rinard; Vikash K. Mansinghka (Proc. ACM Program. Lang., 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., 2023)

Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning. - Tan Zhi-Xuan; Lance Ying; Vikash Mansinghka 001; Joshua B. Tenenbaum (AAMAS, 2024)

Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness. - Aidan Curtis; George Matheos; Nishad Gothoskar; Vikash Mansinghka 001; Joshua B. Tenenbaum; Tomás Lozano-Pérez; Leslie Pack Kaelbling (Robotics: Science and Systems, 2024)

Grounding Language about Belief in a Bayesian Theory-of-Mind. - Lance Ying; Tan Zhi-Xuan; Lionel Wong; Vikash Mansinghka 001; Joshua B. Tenenbaum (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 001; 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 002; Cedegao E. Zhang; Tan Zhi-Xuan; Mark K. Ho; Vikash Mansinghka 001; Adrian Weller; Joshua B. Tenenbaum; et al. (CoRR, 2024)

Understanding Epistemic Language with a Bayesian Theory of Mind. - Lance Ying; Tan Zhi-Xuan; Lionel Wong; Vikash Mansinghka 001; Joshua B. Tenenbaum (CoRR, 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)

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)

Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo. - João Loula; Benjamin LeBrun; Li Du; Ben Lipkin; Clemente Pasti; Gabriel Grand; Tianyu Liu 004; Yahya Emara; Marjorie Freedman; Jason Eisner; Ryan Cotterell; Vikash Mansinghka 001; et al. (ICLR, 2025)

Stochastic Lazy Knowledge Compilation for Inference in Discrete Probabilistic Programs. - Maddy Bowers; Alexander K. Lew; Joshua B. Tenenbaum; Armando Solar-Lezama; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2025)

Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind. - Lance Ying; Tan Zhi-Xuan; Lionel Wong; Vikash Mansinghka 001; Joshua B. Tenenbaum (Trans. Assoc. Comput. Linguistics, 2025)

Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling. - Benjamin Lipkin; Benjamin LeBrun; Jacob Hoover Vigly; João Loula; David R. MacIver; Li Du; Jason Eisner; Ryan Cotterell; Vikash Mansinghka 001; Timothy J. O'Donnell; Alexander K. Lew; Tim Vieira (CoRR, 2025)

Self-Steering Language Models. - Gabriel Grand; Joshua B. Tenenbaum; Vikash K. Mansinghka; Alexander K. Lew; Jacob Andreas (CoRR, 2025)

Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality. - Lance Ying; Almog Hillel; Ryan Truong; Vikash K. Mansinghka; Joshua B. Tenenbaum; Tan Zhi-Xuan (CoRR, 2025)