Articles with public access mandates - Vikash K. MansinghkaLearn more
Available somewhere: 27
Gen: A general-purpose probabilistic programming system with programmable inference
MF Cusumano-Towner, FA Saad, A Lew, VK and Mansinghka
Technical Report MIT-CSAIL-TR-2018-020, Computer Science and Artificial …, 2019
Mandates: US Department of Defense
Online bayesian goal inference for boundedly rational planning agents
T Zhi-Xuan, J Mann, T Silver, J Tenenbaum, V Mansinghka
Advances in neural information processing systems 33, 19238-19250, 2020
Mandates: US National Science Foundation, US Department of Defense
Bayesian synthesis of probabilistic programs for automatic data modeling
FA Saad, MF Cusumano-Towner, U Schaechtle, MC Rinard, ...
Proceedings of the ACM on Programming Languages 3 (POPL), 1-32, 2019
Mandates: US Department of Defense
Probabilistic programming with programmable inference
VK Mansinghka, U Schaechtle, S Handa, A Radul, Y Chen, M and Rinard
Proceedings of the 39th ACM SIGPLAN Conference on Programming Language …, 2018
Mandates: US Department of Defense, US Office of the Director of National Intelligence
3DP3: 3D scene perception via probabilistic programming
N Gothoskar, M Cusumano-Towner, B Zinberg, M Ghavamizadeh, ...
Advances in Neural Information Processing Systems 34, 9600-9612, 2021
Mandates: US Department of Defense
Variational particle approximations
A Saeedi, TD Kulkarni, VK Mansinghka, SJ Gershman
Journal of Machine Learning Research 18 (69), 1-29, 2017
Mandates: US Department of Defense
Brain-wide representations of behavior spanning multiple timescales and states in C. elegans
AA Atanas, J Kim, Z Wang, E Bueno, MC Becker, D Kang, J Park, ...
Cell 186 (19), 4134-4151. e31, 2023
Mandates: US National Science Foundation, US Department of Defense, US National …
Elements of a stochastic 3D prediction engine in larval zebrafish prey capture
AD Bolton, M Haesemeyer, J Jordi, U Schaechtle, FA Saad, ...
Elife 8, e51975, 2019
Mandates: US National Institutes of Health
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
M Cusumano-Towner, VK Mansinghka
Advances in Neural Information Processing Systems 30, 2017
Mandates: US Department of Defense, US Office of the Director of National Intelligence
Particle Gibbs with ancestor sampling for probabilistic programs
JW Meent, H Yang, V Mansinghka, F Wood
Artificial Intelligence and Statistics, 986-994, 2015
Mandates: UK Engineering and Physical Sciences Research Council
Causal inference using Gaussian processes with structured latent confounders
S Witty, K Takatsu, D Jensen, V Mansinghka
International Conference on Machine Learning, 10313-10323, 2020
Mandates: US Department of Defense
PClean: Bayesian data cleaning at scale with domain-specific probabilistic programming
A Lew, M Agrawal, D Sontag, V Mansinghka
International conference on artificial intelligence and statistics, 1927-1935, 2021
Mandates: US National Science Foundation, US Department of Defense
ADEV: Sound automatic differentiation of expected values of probabilistic programs
AK Lew, M Huot, S Staton, VK Mansinghka
Proceedings of the ACM on Programming Languages 7 (POPL), 121-153, 2023
Mandates: US National Science Foundation, US Department of Defense, Royal Society UK
Temporally-reweighted Chinese restaurant process mixtures for clustering, imputing, and forecasting multivariate time series
FA Saad, VK and Mansinghka
Proceedings of the 21st International Conference on Artificial Intelligence …, 2018
Mandates: US Department of Defense
Incremental inference for probabilistic programs
MF Cusumano-Towner, B Bichsel, T Gehr, M Vechev, ...
Proceedings of the 39th ACM SIGPLAN Conference on Programming Language …, 2018
Mandates: US Department of Defense
Detecting dependencies in sparse, multivariate databases using probabilistic programming and non-parametric Bayes
F Saad, V Mansinghka
Artificial Intelligence and Statistics, 632-641, 2017
Mandates: US Department of Defense, US Office of the Director of National Intelligence
Smcp3: Sequential monte carlo with probabilistic program proposals
AK Lew, G Matheos, T Zhi-Xuan, M Ghavamizadeh, N Gothoskar, ...
International conference on artificial intelligence and statistics, 7061-7088, 2023
Mandates: US National Science Foundation, US Department of Defense
Recursive Monte Carlo and variational inference with auxiliary variables
AK Lew, M Cusumano-Towner, VK Mansinghka
Uncertainty in Artificial Intelligence, 1096-1106, 2022
Mandates: US National Science Foundation
Leveraging unstructured statistical knowledge in a probabilistic language of thought
AK Lew, MH Tessler, VK Mansinghka, JB Tenenbaum
Proceedings of the annual conference of the cognitive science society, 2020
Mandates: US National Science Foundation, US Department of Defense
A family of exact goodness-of-fit tests for high-dimensional discrete distributions
FA Saad, CE Freer, NL Ackerman, VK and Mansinghka
Proceedings of the 22nd International Conference on Artificial Intelligence …, 2019
Mandates: US Department of Defense
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