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ORCIDVikash Mansinghka 0001
Vikash K. Mansinghka
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- affiliation: Massachusetts Institute of Technologyn (MIT), Cambridge, MA, USA
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2020 – today
- 2024
[j9]Mathieu Huot
, Matin Ghavami, Alexander K. Lew, Ulrich Schaechtle, Cameron E. Freer, Zane Shelby, Martin C. Rinard, Feras A. Saad, Vikash K. Mansinghka:
GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables. Proc. ACM Program. Lang. 8(PLDI): 790-815 (2024)- [j8]McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard, Vikash K. Mansinghka:
Probabilistic Programming with Programmable Variational Inference. Proc. ACM Program. Lang. 8(PLDI): 2123-2147 (2024) - [c40]Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, Joshua B. Tenenbaum:
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning. AAMAS 2024: 2094-2103 - [i64]Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum:
Grounding Language about Belief in a Bayesian Theory-of-Mind. CoRR abs/2402.10416 (2024) - [i63]Tan Zhi-Xuan, Lance Ying, Vikash Mansinghka, Joshua B. Tenenbaum:
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning. CoRR abs/2402.17930 (2024) - [i62]Aidan Curtis, George Matheos, Nishad Gothoskar, Vikash Mansinghka, Joshua B. Tenenbaum, Tomás Lozano-Pérez, Leslie Pack Kaelbling:
Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness. CoRR abs/2403.10454 (2024) - [i61]Mathieu Huot, Matin Ghavami, Alexander K. Lew, Ulrich Schaechtle, Cameron E. Freer, Zane Shelby, Martin C. Rinard, Feras A. Saad, Vikash K. Mansinghka:
GenSQL: A Probabilistic Programming System for Querying Generative Models of Database Tables. CoRR abs/2406.15652 (2024) - [i60]McCoy R. Becker, Alexander K. Lew, Xiaoyan Wang, Matin Ghavami, Mathieu Huot, Martin C. Rinard, Vikash K. Mansinghka:
Probabilistic Programming with Programmable Variational Inference. CoRR abs/2406.15742 (2024) - [i59]Tan Zhi-Xuan, Gloria Kang, Vikash Mansinghka, Joshua B. Tenenbaum:
Infinite Ends from Finite Samples: Open-Ended Goal Inference as Top-Down Bayesian Filtering of Bottom-Up Proposals. CoRR abs/2407.16770 (2024) - [i58]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:
Building Machines that Learn and Think with People. CoRR abs/2408.03943 (2024) - [i57]Lance Ying, Tan Zhi-Xuan, Lionel Wong, Vikash Mansinghka, Joshua B. Tenenbaum:
Understanding Epistemic Language with a Bayesian Theory of Mind. CoRR abs/2408.12022 (2024) - 2023
- [j7]Alexander K. Lew, Matin Ghavamizadeh, Martin C. Rinard, Vikash K. Mansinghka:
Probabilistic Programming with Stochastic Probabilities. Proc. ACM Program. Lang. 7(PLDI): 1708-1732 (2023) - [j6]Alexander K. Lew, Mathieu Huot, Sam Staton, Vikash K. Mansinghka:
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs. Proc. ACM Program. Lang. 7(POPL): 121-153 (2023) - [c39]Alexander K. Lew, George Matheos, Tan Zhi-Xuan, Matin Ghavamizadeh, Nishad Gothoskar, Stuart Russell, Vikash K. Mansinghka:
SMCP3: Sequential Monte Carlo with Probabilistic Program Proposals. AISTATS 2023: 7061-7088 - [c38]Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous:
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. AISTATS 2023: 10425-10444 - [c37]Tan Zhi-Xuan, Paul Stefan Lunis, Nathalie Fernandez Echeverri, Vikash Mansinghka, Josh Tenenbaum:
Language Models as Informative Goal Priors in a Bayesian Theory of Mind. CogSci 2023 - [c36]Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka:
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation. ICCV 2023: 21568-21579 - [c35]Feras Saad, Brian Patton, Matthew Douglas Hoffman, Rif A. Saurous, Vikash Mansinghka:
Sequential Monte Carlo Learning for Time Series Structure Discovery. ICML 2023: 29473-29489 - [c34]Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka, Sam Staton:
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs. LICS 2023: 1-14 - [i56]Guangyao Zhou, Nishad Gothoskar, Lirui Wang, Joshua B. Tenenbaum, Dan Gutfreund, Miguel Lázaro-Gredilla, Dileep George, Vikash K. Mansinghka:
3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics. CoRR abs/2302.03744 (2023) - [i55]Mathieu Huot, Alexander K. Lew, Vikash K. Mansinghka, Sam Staton:
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs. CoRR abs/2302.10636 (2023) - [i54]Alexander K. Lew, Tan Zhi-Xuan, Gabriel Grand, Vikash K. Mansinghka:
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs. CoRR abs/2306.03081 (2023) - [i53]Gaurav Arya, Ruben Seyer, Frank Schäfer, Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka, Chris Rackauckas, Kartik Chandra, Moritz Schauer:
Differentiating Metropolis-Hastings to Optimize Intractable Densities. CoRR abs/2306.07961 (2023) - [i52]Lionel Wong, Gabriel Grand, Alexander K. Lew, Noah D. Goodman, Vikash K. Mansinghka, Jacob Andreas, Joshua B. Tenenbaum:
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought. CoRR abs/2306.12672 (2023) - [i51]Lance Ying, Tan Zhi-Xuan, Vikash Mansinghka, Joshua B. Tenenbaum:
Inferring the Goals of Communicating Agents from Actions and Instructions. CoRR abs/2306.16207 (2023) - [i50]Feras A. Saad, Brian J. Patton, Matthew D. Hoffman, Rif A. Saurous, Vikash K. Mansinghka:
Sequential Monte Carlo Learning for Time Series Structure Discovery. CoRR abs/2307.09607 (2023) - [i49]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:
Bayes3D: fast learning and inference in structured generative models of 3D objects and scenes. CoRR abs/2312.08715 (2023) - 2022
- [c33]Feras Saad, Marco F. Cusumano-Towner, Vikash Mansinghka:
Estimators of Entropy and Information via Inference in Probabilistic Models. AISTATS 2022: 5604-5621 - [c32]Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George:
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. ICRA 2022: 6674-6680 - [c31]Alexander K. Lew, Marco F. Cusumano-Towner, Vikash K. Mansinghka:
Recursive Monte Carlo and variational inference with auxiliary variables. UAI 2022: 1096-1106 - [i48]Nishad Gothoskar, Miguel Lázaro-Gredilla, Yasemin Bekiroglu, Abhishek Agarwal, Joshua B. Tenenbaum, Vikash K. Mansinghka, Dileep George:
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model. CoRR abs/2202.03697 (2022) - [i47]Feras A. Saad, Marco F. Cusumano-Towner, Vikash K. Mansinghka:
Estimators of Entropy and Information via Inference in Probabilistic Models. CoRR abs/2202.12363 (2022) - [i46]Alexander K. Lew, Marco F. Cusumano-Towner, Vikash K. Mansinghka:
Recursive Monte Carlo and Variational Inference with Auxiliary Variables. CoRR abs/2203.02836 (2022) - [i45]Tan Zhi-Xuan, Nishad Gothoskar, Falk Pollok, Dan Gutfreund, Joshua B. Tenenbaum, Vikash K. Mansinghka:
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind. CoRR abs/2208.02914 (2022) - [i44]Tan Zhi-Xuan, Joshua B. Tenenbaum, Vikash K. Mansinghka:
Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning. CoRR abs/2208.02938 (2022) - [i43]Matthew D. Hoffman, Tuan Anh Le, Pavel Sountsov, Christopher Suter, Ben Lee, Vikash K. Mansinghka, Rif A. Saurous:
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images. CoRR abs/2210.17415 (2022) - [i42]Alexander K. Lew, Mathieu Huot, Sam Staton, Vikash K. Mansinghka:
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs. CoRR abs/2212.06386 (2022) - 2021
- [c30]Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash Mansinghka:
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. AISTATS 2021: 1927-1935 - [c29]Arwa Alanqary, Gloria Z. Lin, Joie Le, Tan Zhi-Xuan, Vikash Mansinghka, Josh Tenenbaum:
Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind. CogSci 2021 - [c28]Nishad Gothoskar, Marco F. Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Josh Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka:
3DP3: 3D Scene Perception via Probabilistic Programming. NeurIPS 2021: 9600-9612 - [c27]Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka:
SPPL: probabilistic programming with fast exact symbolic inference. PLDI 2021: 804-819 - [c26]Feras A. Saad, Vikash K. Mansinghka:
Hierarchical infinite relational model. UAI 2021: 1067-1077 - [i41]Sam Witty, David D. Jensen, Vikash Mansinghka:
A Simulation-Based Test of Identifiability for Bayesian Causal Inference. CoRR abs/2102.11761 (2021) - [i40]Arwa Alanqary, Gloria Z. Lin, Joie Le, Tan Zhi-Xuan, Vikash K. Mansinghka, Joshua B. Tenenbaum:
Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind. CoRR abs/2106.13249 (2021) - [i39]Feras A. Saad, Vikash K. Mansinghka:
Hierarchical Infinite Relational Model. CoRR abs/2108.07208 (2021) - [i38]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:
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence. CoRR abs/2110.15245 (2021) - [i37]Nishad Gothoskar, Marco F. Cusumano-Towner, Ben Zinberg, Matin Ghavamizadeh, Falk Pollok, Austin Garrett, Joshua B. Tenenbaum, Dan Gutfreund, Vikash K. Mansinghka:
3DP3: 3D Scene Perception via Probabilistic Programming. CoRR abs/2111.00312 (2021) - [i36]Alexander K. Lew, Mathieu Huot, Vikash K. Mansinghka:
Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages. CoRR abs/2111.15456 (2021) - 2020
- [j5]Alexander K. Lew, Marco F. Cusumano-Towner, Benjamin Sherman, Michael Carbin, Vikash K. Mansinghka:
Trace types and denotational semantics for sound programmable inference in probabilistic languages. Proc. ACM Program. Lang. 4(POPL): 19:1-19:32 (2020) - [j4]Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka:
Optimal approximate sampling from discrete probability distributions. Proc. ACM Program. Lang. 4(POPL): 36:1-36:31 (2020) - [c25]Feras Saad, Cameron E. Freer, Martin C. Rinard, Vikash Mansinghka:
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. AISTATS 2020: 1036-1046 - [c24]Alexander K. Lew, Michael Henry Tessler, Vikash Mansinghka, Josh Tenenbaum:
Leveraging Unstructured Statistical Knowledge in a Probabilistic Language of Thought. CogSci 2020 - [c23]Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka:
Causal Inference using Gaussian Processes with Structured Latent Confounders. ICML 2020: 10313-10323 - [c22]Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Josh Tenenbaum, Vikash Mansinghka:
Online Bayesian Goal Inference for Boundedly Rational Planning Agents. NeurIPS 2020 - [i35]Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka:
Optimal Approximate Sampling from Discrete Probability Distributions. CoRR abs/2001.04555 (2020) - [i34]Feras A. Saad, Cameron E. Freer, Martin C. Rinard, Vikash K. Mansinghka:
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions. CoRR abs/2003.03830 (2020) - [i33]Tan Zhi-Xuan, Jordyn L. Mann, Tom Silver, Joshua B. Tenenbaum, Vikash K. Mansinghka:
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents. CoRR abs/2006.07532 (2020) - [i32]Span Spanbauer, Cameron E. Freer, Vikash Mansinghka:
Deep Involutive Generative Models for Neural MCMC. CoRR abs/2006.15167 (2020) - [i31]Sam Witty, Kenta Takatsu, David D. Jensen, Vikash Mansinghka:
Causal Inference using Gaussian Processes with Structured Latent Confounders. CoRR abs/2007.07127 (2020) - [i30]Alexander K. Lew, Monica Agrawal, David A. Sontag, Vikash K. Mansinghka:
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming. CoRR abs/2007.11838 (2020) - [i29]Feras A. Saad, Martin C. Rinard, Vikash K. Mansinghka:
Exact Symbolic Inference in Probabilistic Programs via Sum-Product Representations. CoRR abs/2010.03485 (2020)
2010 – 2019
- 2019
- [j3]Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka:
Bayesian synthesis of probabilistic programs for automatic data modeling. Proc. ACM Program. Lang. 3(POPL): 37:1-37:32 (2019) - [c21]Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka:
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions. AISTATS 2019: 1640-1649 - [c20]Marco F. Cusumano-Towner, Feras A. Saad, Alexander K. Lew, Vikash K. Mansinghka:
Gen: a general-purpose probabilistic programming system with programmable inference. PLDI 2019: 221-236 - [c19]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:
Usability of Probabilistic Programming Languages. PPIG 2019 - [i28]Feras A. Saad, Cameron E. Freer, Nathanael L. Ackerman, Vikash K. Mansinghka:
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions. CoRR abs/1902.10142 (2019) - [i27]Javier Felip, Nilesh A. Ahuja, David Gómez-Gutiérrez, Omesh Tickoo, Vikash Mansinghka:
Real-time Approximate Bayesian Computation for Scene Understanding. CoRR abs/1905.13307 (2019) - [i26]Shivam Handa, Vikash Mansinghka, Martin C. Rinard:
Compositional Inference Metaprogramming with Convergence Guarantees. CoRR abs/1907.05451 (2019) - [i25]Feras A. Saad, Marco F. Cusumano-Towner, Ulrich Schaechtle, Martin C. Rinard, Vikash K. Mansinghka:
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling. CoRR abs/1907.06249 (2019) - [i24]Sam Witty, Alexander K. Lew, David D. Jensen, Vikash Mansinghka:
Bayesian causal inference via probabilistic program synthesis. CoRR abs/1910.14124 (2019) - 2018
- [c18]Feras Saad, Vikash Mansinghka:
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series. AISTATS 2018: 755-764 - [c17]Marco F. Cusumano-Towner, Vikash K. Mansinghka:
A design proposal for Gen: probabilistic programming with fast custom inference via code generation. MAPL@PLDI 2018: 52-57 - [c16]Marco F. Cusumano-Towner, Benjamin Bichsel, Timon Gehr, Martin T. Vechev, Vikash K. Mansinghka:
Incremental inference for probabilistic programs. PLDI 2018: 571-585 - [c15]Vikash K. Mansinghka, Ulrich Schaechtle, Shivam Handa, Alexey Radul, Yutian Chen, Martin C. Rinard:
Probabilistic programming with programmable inference. PLDI 2018: 603-616 - [i23]Marco F. Cusumano-Towner, Vikash K. Mansinghka:
Using probabilistic programs as proposals. CoRR abs/1801.03612 (2018) - 2017
- [j2]Ardavan Saeedi, Tejas D. Kulkarni, Vikash K. Mansinghka, Samuel J. Gershman:
Variational Particle Approximations. J. Mach. Learn. Res. 18: 69:1-69:29 (2017) - [c14]Feras Saad, Vikash Mansinghka:
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes. AISTATS 2017: 632-641 - [c13]Marco F. Cusumano-Towner, Vikash K. Mansinghka:
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. NIPS 2017: 3000-3010 - [i22]Feras Saad, Leonardo Casarsa, Vikash Mansinghka:
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes. CoRR abs/1704.01087 (2017) - [i21]Marco F. Cusumano-Towner, Alexey Radul, David Wingate, Vikash K. Mansinghka:
Probabilistic programs for inferring the goals of autonomous agents. CoRR abs/1704.04977 (2017) - [i20]Marco F. Cusumano-Towner, Vikash K. Mansinghka:
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. CoRR abs/1705.07224 (2017) - [i19]Feras A. Saad, Vikash K. Mansinghka:
A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series. CoRR abs/1710.06900 (2017) - 2016
- [j1]Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum:
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. J. Mach. Learn. Res. 17: 138:1-138:49 (2016) - [c12]Feras Saad, Vikash K. Mansinghka:
A Probabilistic Programming Approach To Probabilistic Data Analysis. NIPS 2016: 2011-2019 - [i18]Marco F. Cusumano-Towner, Vikash K. Mansinghka:
Quantifying the probable approximation error of probabilistic inference programs. CoRR abs/1606.00068 (2016) - [i17]Feras Saad, Vikash Mansinghka:
Probabilistic Data Analysis with Probabilistic Programming. CoRR abs/1608.05347 (2016) - [i16]Feras Saad, Vikash Mansinghka:
Detecting Dependencies in High-Dimensional, Sparse Databases Using Probabilistic Programming and Non-parametric Bayes. CoRR abs/1611.01708 (2016) - [i15]Marco F. Cusumano-Towner, Vikash K. Mansinghka:
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming. CoRR abs/1612.02161 (2016) - [i14]Marco F. Cusumano-Towner, Vikash K. Mansinghka:
Encapsulating models and approximate inference programs in probabilistic modules. CoRR abs/1612.04759 (2016) - 2015
- [c11]Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. AISTATS 2015 - [c10]Tejas D. Kulkarni, Pushmeet Kohli, Joshua B. Tenenbaum, Vikash Mansinghka:
Picture: A probabilistic programming language for scene perception. CVPR 2015: 4390-4399 - [c9]Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. ICML 2015: 693-701 - [i13]Jan-Willem van de Meent, Hongseok Yang, Vikash Mansinghka, Frank D. Wood:
Particle Gibbs with Ancestor Sampling for Probabilistic Programs. CoRR abs/1501.06769 (2015) - [i12]Jonathan H. Huggins, Karthik Narasimhan, Ardavan Saeedi, Vikash K. Mansinghka:
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes. CoRR abs/1503.00332 (2015) - [i11]Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka:
A New Approach to Probabilistic Programming Inference. CoRR abs/1507.00996 (2015) - [i10]Vikash Mansinghka, Patrick Shafto, Eric Jonas, Cap Petschulat, Max Gasner, Joshua B. Tenenbaum:
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data. CoRR abs/1512.01272 (2015) - [i9]Vikash Mansinghka, Richard Tibbetts, Jay Baxter, Patrick Shafto, Baxter Eaves:
BayesDB: A probabilistic programming system for querying the probable implications of data. CoRR abs/1512.05006 (2015) - [i8]Ulrich Schaechtle, Ben Zinberg, Alexey Radul, Kostas Stathis, Vikash K. Mansinghka:
Probabilistic Programming with Gaussian Process Memoization. CoRR abs/1512.05665 (2015) - 2014
- [c8]Frank D. Wood, Jan-Willem van de Meent, Vikash Mansinghka:
A New Approach to Probabilistic Programming Inference. AISTATS 2014: 1024-1032 - [i7]Vikash Mansinghka, Eric Jonas:
Building fast Bayesian computing machines out of intentionally stochastic, digital parts. CoRR abs/1402.4914 (2014) - [i6]Vikash Mansinghka, Daniel Selsam, Yura N. Perov:
Venture: a higher-order probabilistic programming platform with programmable inference. CoRR abs/1404.0099 (2014) - [i5]Tejas D. Kulkarni, Vikash K. Mansinghka, Pushmeet Kohli, Joshua B. Tenenbaum:
Inverse Graphics with Probabilistic CAD Models. CoRR abs/1407.1339 (2014) - 2013
- [c7]Biplab Deka, Alex A. Birklykke, Henry Duwe, Vikash K. Mansinghka, Rakesh Kumar:
Markov chain algorithms: A template for building future robust low power systems. ACSSC 2013: 118-125 - [c6]Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum:
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. NIPS 2013: 1520-1528 - [i4]Dan Lovell, Jonathan Malmaud, Ryan P. Adams, Vikash K. Mansinghka:
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures. CoRR abs/1304.2302 (2013) - [i3]Vikash K. Mansinghka, Tejas D. Kulkarni, Yura N. Perov, Joshua B. Tenenbaum:
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs. CoRR abs/1307.0060 (2013) - 2012
- [i2]Noah D. Goodman, Vikash Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models. CoRR abs/1206.3255 (2012) - [i1]Vikash Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum:
Structured Priors for Structure Learning. CoRR abs/1206.6852 (2012)
2000 – 2009
- 2009
- [c5]Vikash Mansinghka, Daniel M. Roy, Eric Jonas, Joshua B. Tenenbaum:
Exact and Approximate Sampling by Systematic Stochastic Search. AISTATS 2009: 400-407 - 2008
- [c4]Noah D. Goodman, Vikash K. Mansinghka, Daniel M. Roy, Kallista A. Bonawitz, Joshua B. Tenenbaum:
Church: a language for generative models. UAI 2008: 220-229 - 2007
- [c3]Vikash K. Mansinghka, Daniel M. Roy, Ryan Rifkin, Joshua B. Tenenbaum:
AClass: A simple, online, parallelizable algorithm for probabilistic classification. AISTATS 2007: 315-322 - 2006
- [c2]Daniel M. Roy, Charles Kemp, Vikash Mansinghka, Joshua B. Tenenbaum:
Learning annotated hierarchies from relational data. NIPS 2006: 1185-1192 - [c1]Vikash K. Mansinghka, Charles Kemp, Thomas L. Griffiths, Joshua B. Tenenbaum:
Structured Priors for Structure Learning. UAI 2006
Coauthor Index
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last updated on 2024-10-07 21:20 CEST by the dblp team
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