Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Inference Over Programs That Make Predictions
This abstract extends on the previous work (arXiv:1407.2646, arXiv:1606.00075) on program induction using probabilistic programming. It describes possible further steps to extend that work, such that, ultimately, automat…
Probabilistic ProgrammingProgram inductionProgram SynthesisSinkhorn AutoEncoders
Optimal transport offers an alternative to maximum likelihood for learning generative autoencoding models. We show that minimizing the p-Wasserstein distance between the generator and the true data distribution is equiva…
Probabilistic ProgrammingAutomated learning with a probabilistic programming language: Birch
This work offers a broad perspective on probabilistic modeling and inference in light of recent advances in probabilistic programming, in which models are formally expressed in Turing-complete programming languages. We c…
FormMultiple Object TrackingObject TrackingProbabilistic ProgrammingCompiling Stan to Generative Probabilistic Languages and Extension to Deep Probabilistic Programming
Stan is a probabilistic programming language that is popular in the statistics community, with a high-level syntax for expressing probabilistic models. Stan differs by nature from generative probabilistic programming lan…
Probabilistic ProgrammingRepresentation LearningVariational InferenceAn Introduction to Probabilistic Programming
This book is a graduate-level introduction to probabilistic programming. It not only provides a thorough background for anyone wishing to use a probabilistic programming system, but also introduces the techniques needed …
Probabilistic ProgrammingNeural Distribution Learning for generalized time-to-event prediction
Predicting the time to the next event is an important task in various domains. However, due to censoring and irregularly sampled sequences, time-to-event prediction has resulted in limited success only for particular ta…
PredictionProbabilistic ProgrammingTime-to-Event PredictionScenic: A Language for Scenario Specification and Scene Generation
We propose a new probabilistic programming language for the design and analysis of perception systems, especially those based on machine learning. Specifically, we consider the problems of training a perception system to…
Probabilistic ProgrammingScene GenerationSynthetic Data GenerationA Fairness-aware Hybrid Recommender System
Recommender systems are used in variety of domains affecting people's lives. This has raised concerns about possible biases and discrimination that such systems might exacerbate. There are two primary kinds of biases inh…
FairnessMovie RecommendationProbabilistic ProgrammingRecommendation SystemsMachine Teaching of Active Sequential Learners
Machine teaching addresses the problem of finding the best training data that can guide a learning algorithm to a target model with minimal effort. In conventional settings, a teacher provides data that are consistent wi…
Multi-Armed BanditsProbabilistic ProgrammingEfficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model
We present a novel probabilistic programming framework that couples directly to existing large-scale simulators through a cross-platform probabilistic execution protocol, which allows general-purpose inference engines to…
Bayesian InferenceProbabilistic ProgrammingThe Mathematics of Changing one's Mind, via Jeffrey's or via Pearl's update rule
Evidence in probabilistic reasoning may be 'hard' or 'soft', that is, it may be of yes/no form, or it may involve a strength of belief, in the unit interval [0, 1]. Reasoning with soft, [0, 1]-valued evidence is importan…
Probabilistic ProgrammingSound Abstraction and Decomposition of Probabilistic Programs
Probabilistic programming languages are a flexible tool for specifying statistical models, but this flexibility comes at the cost of efficient analysis. It is currently difficult to compactly represent the subtle in…
Probabilistic ProgrammingDiscrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms
Despite the recent successes of probabilistic programming languages (PPLs) in AI applications, PPLs offer only limited support for random variables whose distributions combine discrete and continuous elements. We develop…
Probabilistic ProgrammingState Space ModelsMyopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic Programming
We design a new myopic strategy for a wide class of sequential design of experiment (DOE) problems, where the goal is to collect data in order to to fulfil a certain problem specific goal. Our approach, Myopic Posterior …
Bayesian InferenceMulti-Armed BanditsProbabilistic ProgrammingReinforcement Learning+2Deep Probabilistic Programming Languages: A Qualitative Study
Deep probabilistic programming languages try to combine the advantages of deep learning with those of probabilistic programming languages. If successful, this would be a big step forward in machine learning and programmi…
BIG-bench Machine LearningProbabilistic ProgrammingHamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
Hamiltonian Monte Carlo (HMC) is arguably the dominant statistical inference algorithm used in most popular "first-order differentiable" Probabilistic Programming Languages (PPLs). However, the fact that HMC uses derivat…
Probabilistic ProgrammingNesting Probabilistic Programs
We formalize the notion of nesting probabilistic programming queries and investigate the resulting statistical implications. We demonstrate that while query nesting allows the definition of models which could not otherwi…
Probabilistic ProgrammingModelling contextuality by probabilistic programs with hypergraph semantics
Models of a phenomenon are often developed by examining it under different experimental conditions, or measurement contexts. The resultant probabilistic models assume that the underlying random variables, which define a …
Probabilistic ProgrammingProbabilistic Planning by Probabilistic Programming
Automated planning is a major topic of research in artificial intelligence, and enjoys a long and distinguished history. The classical paradigm assumes a distinguished initial state, comprised of a set of facts, and is d…
Probabilistic ProgrammingBayesian Neural Networks
This paper describes and discusses Bayesian Neural Network (BNN). The paper showcases a few different applications of them for classification and regression problems. BNNs are comprised of a Probabilistic Model and a Neu…
Probabilistic Programming