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Estimators of Entropy and Information via Inference in Probabilistic Models



Feras A. Saad , Marco Cusumano-Towner , Vikash K. Mansinghka

* 18 pages, 8 figures. Appearing in AISTATS 2022 

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Recursive Monte Carlo and Variational Inference with Auxiliary Variables



Alexander K. Lew , Marco Cusumano-Towner , Vikash K. Mansinghka

* 8 pages, + 16-page supplement 

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DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model



Nishad Gothoskar , Miguel Lázaro-Gredilla , Yasemin Bekiroglu , Abhishek Agarwal , Joshua B. Tenenbaum , Vikash K. Mansinghka , Dileep George


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3DP3: 3D Scene Perception via Probabilistic Programming



Nishad Gothoskar , Marco Cusumano-Towner , Ben Zinberg , Matin Ghavamizadeh , Falk Pollok , Austin Garrett , Joshua B. Tenenbaum , Dan Gutfreund , Vikash K. Mansinghka

* NeurIPS 2021 

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Hierarchical Infinite Relational Model



Feras A. Saad , Vikash K. Mansinghka

* 11 pages, 6 figures, 4 tables. Appearing in UAI 2021 

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Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind



Arwa Alanqary , Gloria Z. Lin , Joie Le , Tan Zhi-Xuan , Vikash K. Mansinghka , Joshua B. Tenenbaum

* Accepted to CogSci 2021. 6 pages, 5 figures. (Appendix: 1 page, 1 figure) 

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Exact Symbolic Inference in Probabilistic Programs via Sum-Product Representations



Feras A. Saad , Martin C. Rinard , Vikash K. Mansinghka


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PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming



Alexander K. Lew , Monica Agrawal , David Sontag , Vikash K. Mansinghka

* Added references; revised abstract 

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