Mansinghka, Vikash K.

16 publications

ICCV 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

AISTATS 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

AISTATS 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

UAI 2022 Recursive Monte Carlo and Variational Inference with Auxiliary Variables Alexander K. Lew, Marco Cusumano-Towner, Vikash K. Mansinghka

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

UAI 2021 Hierarchical Infinite Relational Model Feras A. Saad, Vikash K. Mansinghka

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

AISTATS 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

NeurIPS 2017 AIDE: An Algorithm for Measuring the Accuracy of Probabilistic Inference Algorithms Marco Cusumano-Towner, Vikash K Mansinghka

JMLR 2017 Variational Particle Approximations Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman

NeurIPS 2016 A Probabilistic Programming Approach to Probabilistic Data Analysis Feras Saad, Vikash K Mansinghka

NeurIPS 2013 Approximate Bayesian Image Interpretation Using Generative Probabilistic Graphics Programs Vikash K Mansinghka, Tejas D Kulkarni, Yura N Perov, Josh Tenenbaum

UAI 2008 Church: A Language for Generative Models Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum

AISTATS 2007 AClass: A Simple, Online, Parallelizable Algorithm for Probabilistic Classification Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Josh Tenenbaum

NeurIPS 2006 Learning Annotated Hierarchies from Relational Data Daniel M. Roy, Charles Kemp, Vikash K. Mansinghka, Joshua B. Tenenbaum

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