Marco Cusumano-Towner
Marco Cusumano-Towner
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Cited by
Cited by
Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding
J Maitin-Shepard, M Cusumano-Towner, J Lei, P Abbeel
2010 IEEE International Conference on Robotics and Automation, 2308-2315, 2010
Gen: a general-purpose probabilistic programming system with programmable inference
MF Cusumano-Towner, FA Saad, AK Lew, VK Mansinghka
Proceedings of the 40th acm sigplan conference on programming language …, 2019
Bringing clothing into desired configurations with limited perception
M Cusumano-Towner, A Singh, S Miller, JF O'Brien, P Abbeel
2011 IEEE international conference on robotics and automation, 3893-3900, 2011
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
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
Trace types and denotational semantics for sound programmable inference in probabilistic languages
AK Lew, MF Cusumano-Towner, B Sherman, M Carbin, VK Mansinghka
Proceedings of the ACM on Programming Languages 4 (POPL), 1-32, 2019
A social network of hospital acquired infection built from electronic medical record data
M Cusumano-Towner, DY Li, S Tuo, G Krishnan, DM Maslove
Journal of the American Medical Informatics Association 20 (3), 427-434, 2013
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
M Cusumano-Towner, VK Mansinghka
Advances in Neural Information Processing Systems 30, 2017
Incremental inference for probabilistic programs
M Cusumano-Towner, B Bichsel, T Gehr, M Vechev, VK Mansinghka
Proceedings of the 39th acm sigplan conference on programming language …, 2018
Probabilistic programs for inferring the goals of autonomous agents
MF Cusumano-Towner, A Radul, D Wingate, VK Mansinghka
arXiv preprint arXiv:1704.04977, 2017
Automating involutive MCMC using probabilistic and differentiable programming
M Cusumano-Towner, AK Lew, VK Mansinghka
arXiv preprint arXiv:2007.09871, 2020
Using probabilistic programs as proposals
MF Cusumano-Towner, VK Mansinghka
arXiv preprint arXiv:1801.03612, 2018
Recursive Monte Carlo and variational inference with auxiliary variables
AK Lew, M Cusumano-Towner, VK Mansinghka
Uncertainty in Artificial Intelligence, 1096-1106, 2022
A design proposal for Gen: Probabilistic programming with fast custom inference via code generation
M Cusumano-Towner, VK Mansinghka
Proceedings of the 2nd ACM SIGPLAN International Workshop on Machine …, 2018
Structured differentiable models of 3d scenes via generative scene graphs
B Zinberg, M Cusumano-Towner, KM Vikash
Workshop on Perception as Generative Reasoning, NeurIPS, Submitted September, 2019
Encapsulating models and approximate inference programs in probabilistic modules
MF Cusumano-Towner, VK Mansinghka
arXiv preprint arXiv:1612.04759, 2016
Estimators of entropy and information via inference in probabilistic models
F Saad, M Cusumano-Towner, V Mansinghka
International Conference on Artificial Intelligence and Statistics, 5604-5621, 2022
Transforming worlds: Automated involutive MCMC for open-universe probabilistic models
G Matheos, AK Lew, M Ghavamizadeh, S Russell, M Cusumano-Towner, ...
Third Symposium on Advances in Approximate Bayesian Inference, 2020
Gen: a high-level programming platform for probabilistic inference
MF Cusumano-Towner
Massachusetts Institute of Technology, 2020
Quantifying the probable approximation error of probabilistic inference programs
MF Cusumano-Towner, VK Mansinghka
arXiv preprint arXiv:1606.00068, 2016
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