Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Large Language Bayes
Many domain experts do not have the time or training to write formal Bayesian models. This paper takes an informal problem description as input, and combines a large language model and a probabilistic programming languag…
Language ModelingLanguage ModellingLarge Language ModelProbabilistic Programming+1Syntactic and Semantic Control of Large Language Models via Sequential Monte Carlo
A wide range of LM applications require generating text that conforms to syntactic or semantic constraints. Imposing such constraints can be naturally framed as probabilistic conditioning, but exact generation from the r…
Code GenerationProbabilistic ProgrammingText to SQLText-To-SQLDeepRV: pre-trained spatial priors for accelerated disease mapping
Recently introduced prior-encoding deep generative models (e.g., PriorVAE, $\pi$VAE, and PriorCVAE) have emerged as powerful tools for scalable Bayesian inference by emulating complex stochastic processes like Gaussian p…
Bayesian InferenceDecoderGaussian ProcessesProbabilistic ProgrammingProceedings 40th International Conference on Logic Programming
Since the first conference In Marseille in 1982, the International Conference on Logic Programming (ICLP) has been the premier international event for presenting research in logic programming. These proceedings include t…
Inductive logic programmingProbabilistic ProgrammingPrompt EngineeringRepresentation of Molecules via Algebraic Data Types : Advancing Beyond SMILES & SELFIES
We introduce a novel molecular representation through Algebraic Data Types (ADTs) - composite data structures formed through the combination of simpler types that obey algebraic laws. By explicitly considering how the da…
molecular representationProbabilistic ProgrammingHamiltonian Monte Carlo Inference of Marginalized Linear Mixed-Effects Models
Bayesian reasoning in linear mixed-effects models (LMMs) is challenging and often requires advanced sampling techniques like Markov chain Monte Carlo (MCMC). A common approach is to write the model in a probabilistic pro…
Probabilistic ProgrammingA Distribution Semantics for Probabilistic Term Rewriting
Probabilistic programming is becoming increasingly popular thanks to its ability to specify problems with a certain degree of uncertainty. In this work, we focus on term rewriting, a well-known computational formalism. I…
Probabilistic ProgrammingIdentifying latent disease factors differently expressed in patient subgroups using group factor analysis
In this study, we propose a novel approach to uncover subgroup-specific and subgroup-common latent factors addressing the challenges posed by the heterogeneity of neurological and mental disorders, which hinder disease u…
Probabilistic ProgrammingInference Plans for Hybrid Particle Filtering
Advanced probabilistic programming languages (PPLs) using hybrid particle filtering combine symbolic exact inference and Monte Carlo methods to improve inference performance. These systems use heuristics to partition ran…
Probabilistic ProgrammingMarkov Senior -- Learning Markov Junior Grammars to Generate User-specified Content
Markov Junior is a probabilistic programming language used for procedural content generation across various domains. However, its reliance on manually crafted and tuned probabilistic rule sets, also called grammars, pres…
Probabilistic ProgrammingProbabilistic Parameter Estimators and Calibration Metrics for Pose Estimation from Image Features
This paper addresses the challenge of probabilistic parameter estimation given measurement uncertainty in real-time. We provide a general formulation and apply this to pose estimation for an autonomous visual landing sys…
parameter estimationPose EstimationProbabilistic ProgrammingQuerying Labeled Time Series Data with Scenario Programs
In order to ensure autonomous vehicles are safe for on-road deployment, simulation-based testing has become an integral complement to on-road testing. The rise in simulation testing and validation reflects a growing need…
Autonomous DrivingAutonomous VehiclesProbabilistic ProgrammingTime SeriesProbabilistic Programming with Programmable Variational Inference
Compared to the wide array of advanced Monte Carlo methods supported by modern probabilistic programming languages (PPLs), PPL support for variational inference (VI) is less developed: users are typically limited to a pr…
Probabilistic ProgrammingVariational InferenceData Petri Nets meet Probabilistic Programming (Extended version)
Probabilistic programming (PP) is a programming paradigm that allows for writing statistical models like ordinary programs, performing simulations by running those programs, and analyzing and refining their statistical b…
Probabilistic ProgrammingTranslationThe future of cosmological likelihood-based inference: accelerated high-dimensional parameter estimation and model comparison
We advocate for a new paradigm of cosmological likelihood-based inference, leveraging recent developments in machine learning and its underlying technology, to accelerate Bayesian inference in high-dimensional settings. …
Bayesian InferenceCPUModel Selectionparameter estimation+1Simplifying debiased inference via automatic differentiation and probabilistic programming
We introduce an algorithm that simplifies the construction of efficient estimators, making them accessible to a broader audience. 'Dimple' takes as input computer code representing a parameter of interest and outputs an …
Probabilistic ProgrammingScenicNL: Generating Probabilistic Scenario Programs from Natural Language
For cyber-physical systems (CPS), including robotics and autonomous vehicles, mass deployment has been hindered by fatal errors that occur when operating in rare events. To replicate rare events such as vehicle crashes, …
Autonomous VehiclesProbabilistic ProgrammingCOBRA-PPM: A Causal Bayesian Reasoning Architecture Using Probabilistic Programming for Robot Manipulation Under Uncertainty
Manipulation tasks require robots to reason about cause and effect when interacting with objects. Yet, many data-driven approaches lack causal semantics and thus only consider correlations. We introduce COBRA-PPM, a nove…
Causal InferenceDecision MakingProbabilistic ProgrammingRobot ManipulationAutomated Efficient Estimation using Monte Carlo Efficient Influence Functions
Many practical problems involve estimating low dimensional statistical quantities with high-dimensional models and datasets. Several approaches address these estimation tasks based on the theory of influence functions, s…
Probabilistic ProgrammingBlackJAX: Composable Bayesian inference in JAX
BlackJAX is a library implementing sampling and variational inference algorithms commonly used in Bayesian computation. It is designed for ease of use, speed, and modularity by taking a functional approach to the algorit…
Bayesian InferenceProbabilistic ProgrammingVariational Inference