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Found 33 papers, 14 papers with code
Sequential Monte Carlo Learning for Time Series Structure Discovery
This paper presents a new approach to automatically discovering accurate models of complex time series data.
From Word Models to World Models: Translating from Natural Language to the Probabilistic Language of Thought
Our architecture integrates two computational tools that have not previously come together: we model thinking with probabilistic programs, an expressive representation for commonsense reasoning; and we model meaning construction with large language models (LLMs), which support broad-coverage translation from natural language utterances to code expressions in a probabilistic programming language.
Differentiating Metropolis-Hastings to Optimize Intractable Densities
We develop an algorithm for automatic differentiation of Metropolis-Hastings samplers, allowing us to differentiate through probabilistic inference, even if the model has discrete components within it.
Sequential Monte Carlo Steering of Large Language Models using Probabilistic Programs
Even after fine-tuning and reinforcement learning, large language models (LLMs) can be difficult, if not impossible, to control reliably with prompts alone.
$ω$PAP Spaces: Reasoning Denotationally About Higher-Order, Recursive Probabilistic and Differentiable Programs
We introduce a new setting, the category of $\omega$PAP spaces, for reasoning denotationally about expressive differentiable and probabilistic programming languages.
3D Neural Embedding Likelihood: Probabilistic Inverse Graphics for Robust 6D Pose Estimation
In this paper, we introduce probabilistic modeling to the inverse graphics framework to quantify uncertainty and achieve robustness in 6D pose estimation tasks.
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ProbNeRF: Uncertainty-Aware Inference of 3D Shapes from 2D Images
The problem of inferring object shape from a single 2D image is underconstrained.
Abstract Interpretation for Generalized Heuristic Search in Model-Based Planning
Domain-general model-based planners often derive their generality by constructing search heuristics through the relaxation or abstraction of symbolic world models.
Solving the Baby Intuitions Benchmark with a Hierarchically Bayesian Theory of Mind
To facilitate the development of new models to bridge the gap between machine and human social intelligence, the recently proposed Baby Intuitions Benchmark (arXiv:2102. 11938) provides a suite of tasks designed to evaluate commonsense reasoning about agents' goals and actions that even young infants exhibit.
Recursive Monte Carlo and Variational Inference with Auxiliary Variables
A key design constraint when implementing Monte Carlo and variational inference algorithms is that it must be possible to cheaply and exactly evaluate the marginal densities of proposal distributions and variational families.
Estimators of Entropy and Information via Inference in Probabilistic Models
Estimating information-theoretic quantities such as entropy and mutual information is central to many problems in statistics and machine learning, but challenging in high dimensions.
3DP3: 3D Scene Perception via Probabilistic Programming
We present 3DP3, a framework for inverse graphics that uses inference in a structured generative model of objects, scenes, and images.
Hierarchical Infinite Relational Model
This paper describes the hierarchical infinite relational model (HIRM), a new probabilistic generative model for noisy, sparse, and heterogeneous relational data.
Modeling the Mistakes of Boundedly Rational Agents Within a Bayesian Theory of Mind
Here, we extend the Bayesian Theory of Mind framework to model boundedly rational agents who may have mistaken goals, plans, and actions.
SPPL: Probabilistic Programming with Fast Exact Symbolic Inference
We present the Sum-Product Probabilistic Language (SPPL), a new probabilistic programming language that automatically delivers exact solutions to a broad range of probabilistic inference queries.
PClean: Bayesian Data Cleaning at Scale with Domain-Specific Probabilistic Programming
Data cleaning is naturally framed as probabilistic inference in a generative model of ground-truth data and likely errors, but the diversity of real-world error patterns and the hardness of inference make Bayesian approaches difficult to automate.
Automating Involutive MCMC using Probabilistic and Differentiable Programming
Involutive MCMC is a unifying mathematical construction for MCMC kernels that generalizes many classic and state-of-the-art MCMC algorithms, from reversible jump MCMC to kernels based on deep neural networks.
Computation
Online Bayesian Goal Inference for Boundedly-Rational Planning Agents
These models are specified as probabilistic programs, allowing us to represent and perform efficient Bayesian inference over an agent's goals and internal planning processes.
Bayesian Synthesis of Probabilistic Programs for Automatic Data Modeling
These techniques work with probabilistic domain-specific data modeling languages that capture key properties of a broad class of data generating processes, using Bayesian inference to synthesize probabilistic programs in these modeling languages given observed data.
A Family of Exact Goodness-of-Fit Tests for High-Dimensional Discrete Distributions
Unlike most existing test statistics, the proposed test statistic is distribution-free and its exact (non-asymptotic) sampling distribution is known in closed form.
Using probabilistic programs as proposals
Monte Carlo inference has asymptotic guarantees, but can be slow when using generic proposals.
Temporally-Reweighted Chinese Restaurant Process Mixtures for Clustering, Imputing, and Forecasting Multivariate Time Series
We apply the technique to challenging forecasting and imputation tasks using seasonal flu data from the US Center for Disease Control and Prevention, demonstrating superior forecasting accuracy and competitive imputation accuracy as compared to multiple widely used baselines.
AIDE: An algorithm for measuring the accuracy of probabilistic inference algorithms
This paper introduces the auxiliary inference divergence estimator (AIDE), an algorithm for measuring the accuracy of approximate inference algorithms.
Probabilistic programs for inferring the goals of autonomous agents
Intelligent systems sometimes need to infer the probable goals of people, cars, and robots, based on partial observations of their motion.
Encapsulating models and approximate inference programs in probabilistic modules
This paper introduces the probabilistic module interface, which allows encapsulation of complex probabilistic models with latent variables alongside custom stochastic approximate inference machinery, and provides a platform-agnostic abstraction barrier separating the model internals from the host probabilistic inference system.
Measuring the non-asymptotic convergence of sequential Monte Carlo samplers using probabilistic programming
A key limitation of sampling algorithms for approximate inference is that it is difficult to quantify their approximation error.
A Probabilistic Programming Approach To Probabilistic Data Analysis
Probabilistic techniques are central to data analysis, but different approaches can be challenging to apply, combine, and compare.
Quantifying the probable approximation error of probabilistic inference programs
This paper introduces a new technique for quantifying the approximation error of a broad class of probabilistic inference programs, including ones based on both variational and Monte Carlo approaches.
Probabilistic Programming with Gaussian Process Memoization
Gaussian Processes (GPs) are widely used tools in statistics, machine learning, robotics, computer vision, and scientific computation.
JUMP-Means: Small-Variance Asymptotics for Markov Jump Processes
We derive the small-variance asymptotics for parametric and nonparametric MJPs for both directly observed and hidden state models.
Inverse Graphics with Probabilistic CAD Models
We show that it is possible to solve challenging, real-world 3D vision problems by approximate inference in generative models for images based on rendering the outputs of probabilistic CAD (PCAD) programs.
Approximate Bayesian Image Interpretation using Generative Probabilistic Graphics Programs
The idea of computer vision as the Bayesian inverse problem to computer graphics has a long history and an appealing elegance, but it has proved difficult to directly implement.
ClusterCluster: Parallel Markov Chain Monte Carlo for Dirichlet Process Mixtures
Applied to mixture modeling, our approach enables the Dirichlet process to simultaneously learn clusters that describe the data and superclusters that define the granularity of parallelization.
