Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding. - Jeremy Maitin-Shepard; Marco F. Cusumano-Towner; Jinna Lei; Pieter Abbeel (ICRA, 2010)

Bringing clothing into desired configurations with limited perception. - Marco F. Cusumano-Towner; Arjun Singh; Stephen Miller; James F. O'Brien; Pieter Abbeel (ICRA, 2011)

A social network of hospital acquired infection built from electronic medical record data. - Marco F. Cusumano-Towner; Daniel Y. Li; Shanshan Tuo; Gomathi Krishnan; David M. Maslove (J. Am. Medical Informatics Assoc., 2013)

Quantifying the probable approximation error of probabilistic inference programs. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (CoRR, 2016)

Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (CoRR, 2016)

Encapsulating models and approximate inference programs in probabilistic modules. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (CoRR, 2016)

AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (NIPS, 2017)

Probabilistic programs for inferring the goals of autonomous agents. - Marco F. Cusumano-Towner; Alexey Radul; David Wingate; Vikash K. Mansinghka (CoRR, 2017)

A design proposal for Gen: probabilistic programming with fast custom inference via code generation. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (MAPL@PLDI, 2018)

Incremental inference for probabilistic programs. - Marco F. Cusumano-Towner; Benjamin Bichsel; Timon Gehr; Martin T. Vechev; Vikash K. Mansinghka (PLDI, 2018)

Using probabilistic programs as proposals. - Marco F. Cusumano-Towner; Vikash K. Mansinghka (CoRR, 2018)

Gen: a general-purpose probabilistic programming system with programmable inference. - Marco F. Cusumano-Towner; Feras A. Saad; Alexander K. Lew; Vikash K. Mansinghka (PLDI, 2019)

Bayesian synthesis of probabilistic programs for automatic data modeling. - Feras A. Saad; Marco F. Cusumano-Towner; Ulrich Schaechtle; Martin C. Rinard; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2019)

Trace types and denotational semantics for sound programmable inference in probabilistic languages. - Alexander K. Lew; Marco F. Cusumano-Towner; Benjamin Sherman; Michael Carbin; Vikash K. Mansinghka (Proc. ACM Program. Lang., 2020)

3DP3: 3D Scene Perception via Probabilistic Programming. - Nishad Gothoskar; Marco F. Cusumano-Towner; Ben Zinberg; Matin Ghavamizadeh; Falk Pollok; Austin Garrett; Josh Tenenbaum 001; Dan Gutfreund; Vikash K. Mansinghka (NeurIPS, 2021)

Estimators of Entropy and Information via Inference in Probabilistic Models. - Feras Saad; Marco F. Cusumano-Towner; Vikash Mansinghka 001 (AISTATS, 2022)

Recursive Monte Carlo and variational inference with auxiliary variables. - Alexander K. Lew; Marco F. Cusumano-Towner; Vikash K. Mansinghka (UAI, 2022)

Reinforcement Learning for Long-Horizon Interactive LLM Agents. - Kevin Chen; Marco F. Cusumano-Towner; Brody Huval; Aleksei Petrenko; Jackson Hamburger; Vladlen Koltun; Philipp Krähenbühl (CoRR, 2025)

Robust Autonomy Emerges from Self-Play. - Marco F. Cusumano-Towner; David Hafner; Alexander Hertzberg; Brody Huval; Aleksei Petrenko; Eugene Vinitsky; Erik Wijmans; Taylor Killian; Stuart Bowers; Ozan Sener; Philipp Krähenbühl; Vladlen Koltun (CoRR, 2025)