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From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence


Oct 28, 2021
Nicholas Roy, Ingmar Posner, Tim Barfoot, Philippe Beaudoin, Yoshua Bengio, Jeannette Bohg, Oliver Brock, Isabelle Depatie, Dieter Fox, Dan Koditschek, Tomas Lozano-Perez, Vikash Mansinghka, Christopher Pal, Blake Richards, Dorsa Sadigh, Stefan Schaal, Gaurav Sukhatme, Denis Therien, Marc Toussaint, Michiel Van de Panne


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A Simulation-Based Test of Identifiability for Bayesian Causal Inference


Feb 23, 2021
Sam Witty, David Jensen, Vikash Mansinghka

* 16 pages, 5 figures 

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Causal Inference using Gaussian Processes with Structured Latent Confounders


Jul 14, 2020
Sam Witty, Kenta Takatsu, David Jensen, Vikash Mansinghka

* to be published at ICML2020 

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Deep Involutive Generative Models for Neural MCMC


Jul 02, 2020
Span Spanbauer, Cameron Freer, Vikash Mansinghka

* 13 pages, 6 figures. Revised discussion of the Jacobian determinant factor in the acceptance ratio 

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Bayesian causal inference via probabilistic program synthesis


Oct 30, 2019
Sam Witty, Alexander Lew, David Jensen, Vikash Mansinghka


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Real-time Approximate Bayesian Computation for Scene Understanding


May 22, 2019
Javier Felip, Nilesh Ahuja, David Gómez-Gutiérrez, Omesh Tickoo, Vikash Mansinghka


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Time Series Structure Discovery via Probabilistic Program Synthesis


May 22, 2017
Ulrich Schaechtle, Feras Saad, Alexey Radul, Vikash Mansinghka

* The first two authors contributed equally to this work 

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Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes


Apr 04, 2017
Feras Saad, Leonardo Casarsa, Vikash Mansinghka


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Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes


Mar 27, 2017
Feras Saad, Vikash Mansinghka

* Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, PMLR 54:632-641, 2017 

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