Marco Cusumano-Towner
Marco Cusumano-Towner
Graduate Student, MIT
Verified email at mit.edu - Homepage
Title
Cited by
Cited by
Year
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
3822010
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
1332011
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
482019
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
292013
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
202019
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
142018
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
102019
Probabilistic programs for inferring the goals of autonomous agents
MF Cusumano-Towner, A Radul, D Wingate, VK Mansinghka
arXiv preprint arXiv:1704.04977, 2017
102017
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
M Cusumano-Towner, VK Mansinghka
Advances in Neural Information Processing Systems, 3000-3010, 2017
102017
Using probabilistic programs as proposals
MF Cusumano-Towner, VK Mansinghka
arXiv preprint arXiv:1801.03612, 2018
82018
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
72018
Quantifying the probable approximation error of probabilistic inference programs
MF Cusumano-Towner, VK Mansinghka
arXiv preprint arXiv:1606.00068, 2016
62016
Encapsulating models and approximate inference programs in probabilistic modules
MF Cusumano-Towner, VK Mansinghka
arXiv preprint arXiv:1612.04759, 2016
52016
Automating involutive mcmc using probabilistic and differentiable programming
M Cusumano-Towner, AK Lew, VK Mansinghka
arXiv preprint arXiv:2007.09871, 2020
42020
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
MF Cusumano-Towner, VK Mansinghka
arXiv preprint arXiv:1612.02161, 2016
22016
Using probabilistic programs as proposals. arXiv, 2018
MF Cusumano-Towner, KM Vikash
URL http://arxiv. org/abs, 1801
21801
Structured differentiable models of 3D scenes via generative scene graphs
B Zinberg, M Cusumano-Towner, VK Mansinghka
1
Gen: a high-level programming platform for probabilistic inference
MF Cusumano-Towner
Massachusetts Institute of Technology, 2020
2020
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Articles 1–18