Search Results for author:
Found 27 papers, 5 papers with code
Detecting Dependencies in Sparse, Multivariate Databases Using Probabilistic Programming and Non-parametric Bayes
Datasets with hundreds of variables and many missing values are commonplace.
Probabilistic Search for Structured Data via Probabilistic Programming and Nonparametric Bayes
We found that human evaluators often prefer the results from probabilistic search to results from a standard baseline.
Probabilistic Data Analysis with Probabilistic Programming
This paper introduces composable generative population models (CGPMs), a computational abstraction that extends directed graphical models and can be used to describe and compose a broad class of probabilistic data analysis techniques.
Pragmatic Instruction Following and Goal Assistance via Cooperative Language-Guided Inverse Planning
Our agent assists a human by modeling them as a cooperative planner who communicates joint plans to the assistant, then performs multimodal Bayesian inference over the human's goal from actions and language, using large language models (LLMs) to evaluate the likelihood of an instruction given a hypothesized plan.
CrossCat: A Fully Bayesian Nonparametric Method for Analyzing Heterogeneous, High Dimensional Data
CrossCat infers multiple non-overlapping views of the data, each consisting of a subset of the variables, and uses a separate nonparametric mixture to model each view.
Time Series Structure Discovery via Probabilistic Program Synthesis
There is a widespread need for techniques that can discover structure from time series data.
BayesDB: A probabilistic programming system for querying the probable implications of data
Is it possible to make statistical inference broadly accessible to non-statisticians without sacrificing mathematical rigor or inference quality?
Variational Particle Approximations
Like Monte Carlo, DPVI can handle multiple modes, and yields exact results in a well-defined limit.
A New Approach to Probabilistic Programming Inference
We introduce and demonstrate a new approach to inference in expressive probabilistic programming languages based on particle Markov chain Monte Carlo.
Automatic Inference for Inverting Software Simulators via Probabilistic Programming
Models of complex systems are often formalized as sequential software simulators: computationally intensive programs that iteratively build up probable system configurations given parameters and initial conditions.
Sublinear-Time Approximate MCMC Transitions for Probabilistic Programs
Probabilistic programming languages can simplify the development of machine learning techniques, but only if inference is sufficiently scalable.
Particle Gibbs with Ancestor Sampling for Probabilistic Programs
Particle Markov chain Monte Carlo techniques rank among current state-of-the-art methods for probabilistic program inference.
Church: a language for generative models
We introduce Church, a universal language for describing stochastic generative processes.
Venture: a higher-order probabilistic programming platform with programmable inference
Like Church, probabilistic models and inference problems in Venture are specified via a Turing-complete, higher-order probabilistic language descended from Lisp.
Building fast Bayesian computing machines out of intentionally stochastic, digital parts
Here we show how to build fast Bayesian computing machines using intentionally stochastic, digital parts, narrowing this efficiency gap by multiple orders of magnitude.
Picture: A Probabilistic Programming Language for Scene Perception
Recent progress on probabilistic modeling and statistical learning, coupled with the availability of large training datasets, has led to remarkable progress in computer vision.
Real-time Approximate Bayesian Computation for Scene Understanding
The underlying generative models are built from realistic simulation software, wrapped in a Bayesian error model for the gap between simulation outputs and real data.
Bayesian causal inference via probabilistic program synthesis
This approach makes it straightforward to incorporate data from atomic interventions, as well as shift interventions, variance-scaling interventions, and other interventions that modify causal structure.
Deep Involutive Generative Models for Neural MCMC
We introduce deep involutive generative models, a new architecture for deep generative modeling, and use them to define Involutive Neural MCMC, a new approach to fast neural MCMC.
Causal Inference using Gaussian Processes with Structured Latent Confounders
Latent confounders---unobserved variables that influence both treatment and outcome---can bias estimates of causal effects.
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.
SBI: A Simulation-Based Test of Identifiability for Bayesian Causal Inference
This paper introduces simulation-based identifiability (SBI), a procedure for testing the identifiability of queries in Bayesian causal inference approaches that are implemented as probabilistic programs.
From Machine Learning to Robotics: Challenges and Opportunities for Embodied Intelligence
Machine learning has long since become a keystone technology, accelerating science and applications in a broad range of domains.
Transforming Worlds: Automated Involutive MCMC for Open-Universe Probabilistic Models
Open-universe probabilistic models enable Bayesian inference about how many objects underlie data, and how they are related.
Inferring the Goals of Communicating Agents from Actions and Instructions
When humans cooperate, they frequently coordinate their activity through both verbal communication and non-verbal actions, using this information to infer a shared goal and plan.
Grounding Language about Belief in a Bayesian Theory-of-Mind
In this paper, we take a step towards an answer by grounding the semantics of belief statements in a Bayesian theory-of-mind: By modeling how humans jointly infer coherent sets of goals, beliefs, and plans that explain an agent's actions, then evaluating statements about the agent's beliefs against these inferences via epistemic logic, our framework provides a conceptual role semantics for belief, explaining the gradedness and compositionality of human belief attributions, as well as their intimate connection with goals and plans.
Partially Observable Task and Motion Planning with Uncertainty and Risk Awareness
We propose a strategy for TAMP with Uncertainty and Risk Awareness (TAMPURA) that is capable of efficiently solving long-horizon planning problems with initial-state and action outcome uncertainty, including problems that require information gathering and avoiding undesirable and irreversible outcomes.
