Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Black Box Variational Inference with a Deterministic Objective: Faster, More Accurate, and Even More Black Box
Automatic differentiation variational inference (ADVI) offers fast and easy-to-use posterior approximation in multiple modern probabilistic programming languages. However, its stochastic optimizer lacks clear convergence…
Probabilistic ProgrammingStochastic OptimizationVariational InferenceProbabilistic unifying relations for modelling epistemic and aleatoric uncertainty: semantics and automated reasoning with theorem proving
Probabilistic programming combines general computer programming, statistical inference, and formal semantics to help systems make decisions when facing uncertainty. Probabilistic programs are ubiquitous, including having…
Automated Theorem ProvingProbabilistic ProgrammingNeural Probabilistic Logic Programming in Discrete-Continuous Domains
Neural-symbolic AI (NeSy) allows neural networks to exploit symbolic background knowledge in the form of logic. It has been shown to aid learning in the limited data regime and to facilitate inference on out-of-distribut…
Probabilistic ProgrammingDeclarative Probabilistic Logic Programming in Discrete-Continuous Domains
Over the past three decades, the logic programming paradigm has been successfully expanded to support probabilistic modeling, inference and learning. The resulting paradigm of probabilistic logic programming (PLP) and it…
Probabilistic Programming$ω$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. Our semantics is general enough to assign meanings to …
Probabilistic ProgrammingIncorporating Expert Opinion on Observable Quantities into Statistical Models -- A General Framework
This article describes an approach to incorporate expert opinion on observable quantities through the use of a loss function which updates a prior belief as opposed to specifying parameters on the priors. Eliciting infor…
Probabilistic ProgrammingAutomatically Marginalized MCMC in Probabilistic Programming
Hamiltonian Monte Carlo (HMC) is a powerful algorithm to sample latent variables from Bayesian models. The advent of probabilistic programming languages (PPLs) frees users from writing inference algorithms and lets users…
Probabilistic ProgrammingFast and Correct Gradient-Based Optimisation for Probabilistic Programming via Smoothing
We study the foundations of variational inference, which frames posterior inference as an optimisation problem, for probabilistic programming. The dominant approach for optimisation in practice is stochastic gradient des…
Probabilistic ProgrammingVariational InferenceTreeFlow: probabilistic programming and automatic differentiation for phylogenetics
Probabilistic programming frameworks are powerful tools for statistical modelling and inference. They are not immediately generalisable to phylogenetic problems due to the particular computational properties of the phylo…
Probabilistic ProgrammingDifferentiable Quantum Programming with Unbounded Loops
The emergence of variational quantum applications has led to the development of automatic differentiation techniques in quantum computing. Recently, Zhu et al. (PLDI 2020) have formulated differentiable quantum programmi…
Probabilistic ProgrammingNonparametric Involutive Markov Chain Monte Carlo
A challenging problem in probabilistic programming is to develop inference algorithms that work for arbitrary programs in a universal probabilistic programming language (PPL). We present the nonparametric involutive Mark…
Probabilistic ProgrammingIce Core Dating using Probabilistic Programming
Ice cores record crucial information about past climate. However, before ice core data can have scientific value, the chronology must be inferred by estimating the age as a function of depth. Under certain conditions, ch…
Probabilistic ProgrammingImproved Marginal Unbiased Score Expansion (MUSE) via Implicit Differentiation
We apply the technique of implicit differentiation to boost performance, reduce numerical error, and remove required user-tuning in the Marginal Unbiased Score Expansion (MUSE) algorithm for hierarchical Bayesian inferen…
Bayesian InferenceProbabilistic ProgrammingRobust leave-one-out cross-validation for high-dimensional Bayesian models
Leave-one-out cross-validation (LOO-CV) is a popular method for estimating out-of-sample predictive accuracy. However, computing LOO-CV criteria can be computationally expensive due to the need to fit the model multiple …
Probabilistic ProgrammingVocal Bursts Intensity PredictionBorch: A Deep Universal Probabilistic Programming Language
Ever since the Multilayered Perceptron was first introduced the connectionist community has struggled with the concept of uncertainty and how this could be represented in these types of models. This past decade has seen …
Probabilistic ProgrammingWhen Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
Machine learning (ML) is becoming increasingly crucial in many fields of engineering but has not yet played out its full potential in bioprocess engineering. While experimentation has been accelerated by increasing level…
Model SelectionProbabilistic ProgrammingLearning and Compositionality: a Unification Attempt via Connectionist Probabilistic Programming
We consider learning and compositionality as the key mechanisms towards simulating human-like intelligence. While each mechanism is successfully achieved by neural networks and symbolic AIs, respectively, it is the combi…
Bayesian InferenceProbabilistic ProgrammingMulti-Model Probabilistic Programming
Probabilistic programming makes it easy to represent a probabilistic model as a program. Building an individual model, however, is only one step of probabilistic modeling. The broader challenge of probabilistic modeling …
modelProbabilistic ProgrammingProceedings 38th International Conference on Logic Programming
ICLP is the premier international event for presenting research in logic programming. Contributions to ICLP 2022 were sought in all areas of logic programming, including but not limited to: Foundations: Semantics, Formal…
Automated Theorem ProvingData IntegrationManagementProbabilistic ProgrammingLanguage Model Cascades
Prompted models have demonstrated impressive few-shot learning abilities. Repeated interactions at test-time with a single model, or the composition of multiple models together, further expands capabilities. These compos…
Few-Shot LearningLanguage ModelingLanguage Modellingmodel+1