Vikash Mansinghka | DeepAI

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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...

46 Nicholas Roy, et al. ∙

∙ 02/23/2021

A Simulation-Based Test of Identifiability for Bayesian Causal Inference

This paper introduces a procedure for testing the identifiability of Bay...

0 Sam Witty, et al. ∙

∙ 07/14/2020

Causal Inference using Gaussian Processes with Structured Latent Confounders

Latent confounders—unobserved variables that influence both treatment an...

0 Sam Witty, et al. ∙

∙ 06/26/2020

Deep Involutive Generative Models for Neural MCMC

We introduce deep involutive generative models, a new architecture for d...

6 Span Spanbauer, et al. ∙

∙ 10/30/2019

Bayesian causal inference via probabilistic program synthesis

Causal inference can be formalized as Bayesian inference that combines a...

53 Sam Witty, et al. ∙

∙ 07/11/2019

Compositional Inference Metaprogramming with Convergence Guarantees

Inference metaprogramming enables effective probabilistic programming by...

0 Shivam Handa, et al. ∙

∙ 04/04/2017

Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes

Databases are widespread, yet extracting relevant data can be difficult....

0 Feras Saad, et al. ∙

∙ 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...

0 Feras Saad, et al. ∙

∙ 08/18/2016

Probabilistic Data Analysis with Probabilistic Programming

Probabilistic techniques are central to data analysis, but different app...

0 Feras Saad, et al. ∙

∙ 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...

0 Vikash Mansinghka, et al. ∙

∙ 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...

0 Vikash Mansinghka, et al. ∙

∙ 07/03/2015

A New Approach to Probabilistic Programming Inference

We introduce and demonstrate a new approach to inference in expressive p...

0 Frank Wood, et al. ∙

∙ 05/31/2015

Automatic Inference for Inverting Software Simulators via Probabilistic Programming

Models of complex systems are often formalized as sequential software si...

0 Ardavan Saeedi, et al. ∙

∙ 01/27/2015

Particle Gibbs with Ancestor Sampling for Probabilistic Programs

Particle Markov chain Monte Carlo techniques rank among current state-of...

0 Jan-Willem van de Meent, et al. ∙

∙ 11/06/2014

Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs

Probabilistic programming languages can simplify the development of mach...

0 Yutian Chen, et al. ∙

∙ 04/01/2014

Venture: a higher-order probabilistic programming platform with programmable inference

We describe Venture, an interactive virtual machine for probabilistic pr...

0 Vikash Mansinghka, et al. ∙

∙ 02/24/2014

Variational Particle Approximations

Approximate inference in high-dimensional, discrete probabilistic models...

0 Ardavan Saeedi, et al. ∙

∙ 02/20/2014

Building fast Bayesian computing machines out of intentionally stochastic, digital parts

The brain interprets ambiguous sensory information faster and more relia...

0 Vikash Mansinghka, et al. ∙

∙ 06/27/2012

Structured Priors for Structure Learning

Traditional approaches to Bayes net structure learning typically assume ...

0 Vikash Mansinghka, et al. ∙

∙ 06/13/2012

Church: a language for generative models

We introduce Church, a universal language for describing stochastic gene...

0 Noah Goodman, et al. ∙