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06/28/2023
Inferring the Goals of Communicating Agents from Actions and Instructions
When humans cooperate, they frequently coordinate their activity through...
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10/28/2021
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerati...
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02/23/2021
A Simulation-Based Test of Identifiability for Bayesian Causal Inference
This paper introduces a procedure for testing the identifiability of Bay...
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07/14/2020
Causal Inference using Gaussian Processes with Structured Latent Confounders
Latent confounders—unobserved variables that influence both treatment an...
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06/26/2020
Deep Involutive Generative Models for Neural MCMC
We introduce deep involutive generative models, a new architecture for d...
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10/30/2019
Bayesian causal inference via probabilistic program synthesis
Causal inference can be formalized as Bayesian inference that combines a...
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07/11/2019
Compositional Inference Metaprogramming with Convergence Guarantees
Inference metaprogramming enables effective probabilistic programming by...
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04/04/2017
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
Databases are widespread, yet extracting relevant data can be difficult....
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11/05/2016
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Datasets with hundreds of variables and many missing values are commonpl...
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08/18/2016
Probabilistic Data Analysis with Probabilistic Programming
Probabilistic techniques are central to data analysis, but different app...
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12/15/2015
BayesDB: A probabilistic programming system for querying the probable implications of data
Is it possible to make statistical inference broadly accessible to non-s...
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12/03/2015
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
There is a widespread need for statistical methods that can analyze high...
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07/03/2015
A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive p...
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05/31/2015
Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Models of complex systems are often formalized as sequential software si...
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01/27/2015
Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Particle Markov chain Monte Carlo techniques rank among current state-of...
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11/06/2014
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs
Probabilistic programming languages can simplify the development of mach...
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04/01/2014
Venture: a higher-order probabilistic programming platform with programmable inference
We describe Venture, an interactive virtual machine for probabilistic pr...
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02/24/2014
Variational Particle Approximations
Approximate inference in high-dimensional, discrete probabilistic models...
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02/20/2014
Building fast Bayesian computing machines out of intentionally stochastic, digital parts
The brain interprets ambiguous sensory information faster and more relia...
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06/27/2012
Structured Priors for Structure Learning
Traditional approaches to Bayes net structure learning typically assume ...
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06/13/2012
