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07/13/2023
Sequential Monte Carlo Learning for Time Series Structure Discovery
This paper presents a new approach to automatically discovering accurate...
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06/22/2023
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
How does language inform our downstream thinking? In particular, how do ...
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06/13/2023
Differentiating Metropolis-Hastings to Optimize Intractable Densities
We develop an algorithm for automatic differentiation of Metropolis-Hast...
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06/05/2023
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
Even after fine-tuning and reinforcement learning, large language models...
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02/21/2023
ωPAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs
We introduce a new setting, the category of ωPAP spaces, for reasoning d...
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02/07/2023
3D Neural Embedding Likelihood for Robust Sim-to-Real Transfer in Inverse Graphics
A central challenge in 3D scene perception via inverse graphics is robus...
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12/13/2022
ADEV: Sound Automatic Differentiation of Expected Values of Probabilistic Programs
Optimizing the expected values of probabilistic processes is a central p...
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10/27/2022
ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
The problem of inferring object shape from a single 2D image is undercon...
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08/05/2022
Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning
Domain-general model-based planners often derive their generality by con...
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08/04/2022
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind
To facilitate the development of new models to bridge the gap between ma...
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03/05/2022
Recursive Monte Carlo and Variational Inference with Auxiliary Variables
A key challenge in applying Monte Carlo and variational inference (VI) i...
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02/24/2022
Estimators of Entropy and Information via Inference in Probabilistic Models
Estimating information-theoretic quantities such as entropy and mutual i...
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02/08/2022
DURableVS: Data-efficient Unsupervised Recalibrating Visual Servoing via online learning in a structured generative model
Visual servoing enables robotic systems to perform accurate closed-loop ...
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11/30/2021
Towards Denotational Semantics of AD for Higher-Order, Recursive, Probabilistic Languages
Automatic differentiation (AD) aims to compute derivatives of user-defin...
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10/30/2021
3DP3: 3D Scene Perception via Probabilistic Programming
We present 3DP3, a framework for inverse graphics that uses inference in...
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08/16/2021
Hierarchical Infinite Relational Model
This paper describes the hierarchical infinite relational model (HIRM), ...
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06/24/2021
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...
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10/07/2020
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
We present the Sum-Product Probabilistic Language (SPPL), a new probabil...
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07/23/2020
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Data cleaning can be naturally framed as probabilistic inference in a ge...
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07/20/2020
Automating Involutive MCMC using Probabilistic and Differentiable Programming
Involutive MCMC is a unifying mathematical construction for MCMC kernels...
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06/13/2020
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
People routinely infer the goals of others by observing their actions ov...
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03/08/2020
The Fast Loaded Dice Roller: A Near-Optimal Exact Sampler for Discrete Probability Distributions
This paper introduces a new algorithm for the fundamental problem of gen...
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01/13/2020
Optimal Approximate Sampling from Discrete Probability Distributions
This paper addresses a fundamental problem in random variate generation:...
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07/14/2019
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
We present new techniques for automatically constructing probabilistic p...
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02/26/2019
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions
The objective of goodness-of-fit testing is to assess whether a dataset ...
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01/11/2018
Using probabilistic programs as proposals
Monte Carlo inference has asymptotic guarantees, but can be slow when us...
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10/18/2017
A Bayesian Nonparametric Method for Clustering Imputation, and Forecasting in Multivariate Time Series
This article proposes a Bayesian nonparametric method for forecasting, i...
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05/19/2017
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
Approximate probabilistic inference algorithms are central to many field...
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04/17/2017
Probabilistic programs for inferring the goals of autonomous agents
Intelligent systems sometimes need to infer the probable goals of people...
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12/14/2016
Encapsulating models and approximate inference programs in probabilistic modules
This paper introduces the probabilistic module interface, which allows e...
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12/07/2016
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
A key limitation of sampling algorithms for approximate inference is tha...
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05/31/2016
Quantifying the probable approximation error of probabilistic inference programs
This paper introduces a new technique for quantifying the approximation ...
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12/17/2015
Probabilistic Programming with Gaussian Process Memoization
Gaussian Processes (GPs) are widely used tools in statistics, machine le...
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03/01/2015
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
Markov jump processes (MJPs) are used to model a wide range of phenomena...
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07/04/2014
Inverse Graphics with Probabilistic CAD Models
Recently, multiple formulations of vision problems as probabilistic inve...
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06/29/2013
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
The idea of computer vision as the Bayesian inverse problem to computer ...
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04/08/2013