
Towards Denotational Semantics of AD for HigherOrder, Recursive, Probabilistic Languages
Automatic differentiation (AD) aims to compute derivatives of userdefin...
read it

3DP3: 3D Scene Perception via Probabilistic Programming
We present 3DP3, a framework for inverse graphics that uses inference in...
read it

Hierarchical Infinite Relational Model
This paper describes the hierarchical infinite relational model (HIRM), ...
read it

Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind
When inferring the goals that others are trying to achieve, people intui...
read it

SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
We present the SumProduct Probabilistic Language (SPPL), a new probabil...
read it

PClean: Bayesian Data Cleaning at Scale with DomainSpecific Probabilistic Programming
Data cleaning can be naturally framed as probabilistic inference in a ge...
read it

Automating Involutive MCMC using Probabilistic and Differentiable Programming
Involutive MCMC is a unifying mathematical construction for MCMC kernels...
read it

Online Bayesian Goal Inference for BoundedlyRational Planning Agents
People routinely infer the goals of others by observing their actions ov...
read it

The Fast Loaded Dice Roller: A NearOptimal Exact Sampler for Discrete Probability Distributions
This paper introduces a new algorithm for the fundamental problem of gen...
read it

Optimal Approximate Sampling from Discrete Probability Distributions
This paper addresses a fundamental problem in random variate generation:...
read it

Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
We present new techniques for automatically constructing probabilistic p...
read it

A Family of Exact GoodnessofFit Tests for HighDimensional Discrete Distributions
The objective of goodnessoffit testing is to assess whether a dataset ...
read it

Using probabilistic programs as proposals
Monte Carlo inference has asymptotic guarantees, but can be slow when us...
read it

A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series
This article proposes a Bayesian nonparametric method for forecasting, i...
read it

AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Approximate probabilistic inference algorithms are central to many field...
read it

Probabilistic programs for inferring the goals of autonomous agents
Intelligent systems sometimes need to infer the probable goals of people...
read it

Encapsulating models and approximate inference programs in probabilistic modules
This paper introduces the probabilistic module interface, which allows e...
read it

Measuring the nonasymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
A key limitation of sampling algorithms for approximate inference is tha...
read it

Quantifying the probable approximation error of probabilistic inference programs
This paper introduces a new technique for quantifying the approximation ...
read it

Probabilistic Programming with Gaussian Process Memoization
Gaussian Processes (GPs) are widely used tools in statistics, machine le...
read it

JUMPMeans: SmallVariance Asymptotics for Markov Jump Processes
Markov jump processes (MJPs) are used to model a wide range of phenomena...
read it

Inverse Graphics with Probabilistic CAD Models
Recently, multiple formulations of vision problems as probabilistic inve...
read it

Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
The idea of computer vision as the Bayesian inverse problem to computer ...
read it

ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures
The Dirichlet process (DP) is a fundamental mathematical tool for Bayesi...
read it
Vikash K. Mansinghka
is this you? claim profile