Vikash Mansinghka

56 papers A* 6A 9B 9Journal 29Unranked 3

Year	Rank	Type	Title / Venue / Authors
2025	B	`conf`	Belief Attribution as Mental Explanation: The Role of Accuracy, Informativity, and Causality. CogSci Lance Ying, Almog Hilel, Ryan Truong, Vikash Mansinghka, Joshua B. Tenenbaum, Tan Zhi-Xuan
2025	J	`jnl`	Fast Controlled Generation from Language Models with Adaptive Weighted Rejection Sampling. CoRR Benjamin Lipkin, Benjamin LeBrun, Jacob Hoover Vigly, João Loula, David R. MacIver, Li Du, Jason Eisner, Ryan Cotterell, Vikash Mansinghka, Timothy J. O'Donnell, Alexander K. Lew, Tim Vieira
2025	B	`conf`	Seeing through Occlusion: Uncertainty-aware Joint Physical Tracking and Prediction. CogSci Arijit Dasgupta, Andrew D. Bolton, Vikash Mansinghka, Joshua B. Tenenbaum, Kevin A. Smith
2025	A*	`conf`	Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo. ICLR João 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
2025	J	`jnl`	Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo. CoRR João 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
2025	B	`conf`	Tracking Uncertainty During Uncertain Tracking. CogSci Yoni Friedman, Matin Ghavami, Maddy Bowers, Andrew D. Bolton, Max H. Siegel, Vikash Mansinghka, Joshua B. Tenenbaum
2025	J	`jnl`	Understanding Epistemic Language with a Language-augmented Bayesian Theory of Mind. Trans. Assoc. Comput. Linguistics Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum
2024	J	`jnl`	Building Machines that Learn and Think with People. CoRR 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
2024	B	`conf`	Concept Learning as Coarse-to-Fine Probabilistic Program Induction. CogSci Maddy Bowers, Alexander K. Lew, Wenhao Qi, Joshua S. Rule, Vikash Mansinghka, Josh Tenenbaum, Armando Solar-Lezama
2024	B	`conf`	Grounding Language about Belief in a Bayesian Theory-of-Mind. CogSci Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Josh Tenenbaum
2024	J	`jnl`	Grounding Language about Belief in a Bayesian Theory-of-Mind. CoRR Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum
2024	B	`conf`	Infinite Ends from Finite Samples: Open-Ended Goal Inference as Top-Down Bayesian Filtering of Bottom-Up Proposals. CogSci Tan Zhi-Xuan, Gloria Kang, Vikash Mansinghka, Josh Tenenbaum
2024	J	`jnl`	Infinite Ends from Finite Samples: Open-Ended Goal Inference as Top-Down Bayesian Filtering of Bottom-Up Proposals. CoRR Tan Zhi-Xuan, Gloria Kang, Vikash Mansinghka, Joshua B. Tenenbaum
2024	—	`conf`	Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness. Robotics: Science and Systems Aidan Curtis, George Matheos, Nishad Gothoskar, Vikash Mansinghka, Joshua B. Tenenbaum, Tomás Lozano-Pérez, Leslie Pack Kaelbling
2024	J	`jnl`	Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness. CoRR Aidan Curtis, George Matheos, Nishad Gothoskar, Vikash Mansinghka, Joshua B. Tenenbaum, Tomás Lozano-Pérez, Leslie Pack Kaelbling
2024	A	`conf`	Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning. AAMAS Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, Joshua B. Tenenbaum
2024	J	`jnl`	Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning. CoRR Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, Joshua B. Tenenbaum
2024	J	`jnl`	Understanding Epistemic Language with a Bayesian Theory of Mind. CoRR Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum
2023	J	`jnl`	Inferring the Goals of Communicating Agents from Actions and Instructions. CoRR Lance Ying, Tan Zhi-Xuan, Vikash Mansinghka, Joshua B. Tenenbaum
2023	B	`conf`	Language Models as Informative Goal Priors in a Bayesian Theory of Mind. CogSci Tan Zhi-Xuan, Paul Stefan Lunis, Nathalie Fernandez Echeverri, Vikash Mansinghka, Josh Tenenbaum
2023	A*	`conf`	Sequential Monte Carlo Learning for Time Series Structure Discovery. ICML Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka
2022	A	`conf`	Estimators of Entropy and Information via Inference in Probabilistic Models. AISTATS Feras Saad, Marco F. Cusumano-Towner, Vikash Mansinghka
2021	J	`jnl`	A Simulation-Based Test of Identifiability for Bayesian Causal Inference. CoRR Sam Witty, David D. Jensen, Vikash Mansinghka
2021	J	`jnl`	From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence. CoRR 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
2021	B	`conf`	Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind. CogSci Arwa Alanqary, Gloria Z. Lin, Joie Le, Tan Zhi-Xuan, Vikash Mansinghka, Josh Tenenbaum
2021	A	`conf`	PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. AISTATS Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka
2020	A*	`conf`	Causal Inference using Gaussian Processes with Structured Latent Confounders. ICML Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka
2020	J	`jnl`	Causal Inference using Gaussian Processes with Structured Latent Confounders. CoRR Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka
2020	J	`jnl`	Deep Involutive Generative Models for Neural MCMC. CoRR Span Spanbauer, Cameron E. Freer, Vikash Mansinghka
2020	B	`conf`	Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought. CogSci Alexander K. Lew, Michael Henry Tessler, Vikash Mansinghka, Josh Tenenbaum
2020	A*	`conf`	Online Bayesian Goal Inference for Boundedly Rational Planning Agents. NeurIPS Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Josh Tenenbaum, Vikash Mansinghka
2020	A	`conf`	The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. AISTATS Feras Saad, Cameron E. Freer, Martin C. Rinard, Vikash Mansinghka
2019	J	`jnl`	Bayesian causal inference via probabilistic program synthesis. CoRR Sam Witty, Alexander K. Lew, David D. Jensen, Vikash Mansinghka
2019	J	`jnl`	Compositional Inference Metaprogramming with Convergence Guarantees. CoRR Shivam Handa, Vikash Mansinghka, Martin C. Rinard
2019	J	`jnl`	Real-time Approximate Bayesian Computation for Scene Understanding. CoRR Javier Felip, Nilesh A. Ahuja, David Gómez-Gutiérrez, Omesh Tickoo, Vikash Mansinghka
2019	—	`conf`	Usability of Probabilistic Programming Languages. PPIG 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
2018	A	`conf`	Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series. AISTATS Feras Saad, Vikash Mansinghka
2017	A	`conf`	Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes. AISTATS Feras Saad, Vikash Mansinghka
2017	J	`jnl`	Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes. CoRR Feras Saad, Leonardo Casarsa, Vikash Mansinghka
2016	J	`jnl`	CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. J. Mach. Learn. Res. Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum
2016	J	`jnl`	Detecting Dependencies in High-Dimensional, Sparse Databases Using Probabilistic Programming and Non-parametric Bayes. CoRR Feras Saad, Vikash Mansinghka
2016	J	`jnl`	Probabilistic Data Analysis with Probabilistic Programming. CoRR Feras Saad, Vikash Mansinghka
2015	J	`jnl`	A New Approach to Probabilistic Programming Inference. CoRR Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
2015	J	`jnl`	BayesDB: A probabilistic programming system for querying the probable implications of data. CoRR Vikash Mansinghka, Richard Tibbetts, Jay Baxter, Patrick Shafto, Baxter Eaves
2015	J	`jnl`	CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. CoRR Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum
2015	A*	`conf`	JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka
2015	A	`conf`	Particle Gibbs with Ancestor Sampling for Probabilistic Programs. AISTATS Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood
2015	J	`jnl`	Particle Gibbs with Ancestor Sampling for Probabilistic Programs. CoRR Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood
2015	A*	`conf`	Picture: A probabilistic programming language for scene perception. CVPR Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash Mansinghka
2014	A	`conf`	A New Approach to Probabilistic Programming Inference. AISTATS Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka
2014	J	`jnl`	Building fast Bayesian computing machines out of intentionally stochastic, digital parts. CoRR Vikash Mansinghka, Eric Jonas
2014	J	`jnl`	Venture: a higher-order probabilistic programming platform with programmable inference. CoRR Vikash Mansinghka, Daniel Selsam, Yura N. Perov
2012	J	`jnl`	Church: a language for generative models CoRR Noah D. Goodman, Vikash Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum
2012	J	`jnl`	Structured Priors for Structure Learning. CoRR Vikash Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum
2009	A	`conf`	Exact and Approximate Sampling by Systematic Stochastic Search. AISTATS Vikash Mansinghka, Daniel M. Roy, Eric Jonas, Joshua B. Tenenbaum
2006	—	`conf`	Learning annotated hierarchies from relational data. NIPS Daniel M. Roy, Charles Kemp, Vikash Mansinghka, Joshua B. Tenenbaum