| 2025 | CogSci | Seeing through Occlusion: Uncertainty-aware Joint Physical Tracking and Prediction. | Arijit Dasgupta, Andrew D. Bolton, Vikash Mansinghka, Joshua B. Tenenbaum, Kevin A. Smith |
| 2025 | CogSci | Tracking Uncertainty During Uncertain Tracking. | Yoni Friedman, Matin Ghavami, Maddy Bowers, Andrew D. Bolton, Max H. Siegel, Vikash Mansinghka, Joshua B. Tenenbaum |
| 2025 | CogSci | Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality. | Lance Ying, Almog Hilel, Ryan Truong, Vikash Mansinghka, Joshua B. Tenenbaum, Tan Zhi-Xuan |
| 2025 | ICLR | Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo. | Joo Loula, Benjamin LeBrun, Li Du, Ben Lipkin, Clemente Pasti, Gabriel Grand, Tianyu Liu, Yahya Emara, Marjorie Freedman, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Alexander K. Lew, Tim Vieira, Timothy J. O'Donnell |
| 2024 | CogSci | Concept Learning as Coarse-to-Fine Probabilistic Program Induction. | Maddy Bowers, Alexander K. Lew, Wenhao Qi, Joshua S. Rule, Vikash Mansinghka, Josh Tenenbaum, Armando Solar-Lezama |
| 2024 | CogSci | Grounding Language about Belief in a Bayesian Theory-of-Mind. | Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Josh Tenenbaum |
| 2024 | CogSci | 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, Josh Tenenbaum |
| 2023 | CogSci | 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 |
| 2023 | ICML | Sequential Monte Carlo Learning for Time Series Structure Discovery. | Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka |
| 2022 | AISTATS | Estimators of Entropy and Information via Inference in Probabilistic Models. | Feras Saad, Marco F. Cusumano-Towner, Vikash Mansinghka |
| 2021 | AISTATS | PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. | Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka |
| 2021 | CogSci | 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 |
| 2020 | AISTATS | The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. | Feras Saad, Cameron E. Freer, Martin C. Rinard, Vikash Mansinghka |
| 2020 | CogSci | Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought. | Alexander K. Lew, Michael Henry Tessler, Vikash Mansinghka, Josh Tenenbaum |
| 2020 | ICML | Causal Inference using Gaussian Processes with Structured Latent Confounders. | Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka |
| 2018 | AISTATS | Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series. | Feras Saad, Vikash Mansinghka |
| 2017 | AISTATS | Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes. | Feras Saad, Vikash Mansinghka |
| 2015 | AISTATS | Particle Gibbs with Ancestor Sampling for Probabilistic Programs. | Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood |
| 2015 | CVPR | Picture: A probabilistic programming language for scene perception. | Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash Mansinghka |
| 2015 | ICML | JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. | Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka |
| 2014 | AISTATS | A New Approach to Probabilistic Programming Inference. | Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka |