Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
SLASH: Embracing Probabilistic Circuits into Neural Answer Set Programming
The goal of combining the robustness of neural networks and the expressivity of symbolic methods has rekindled the interest in neuro-symbolic AI. Recent advancements in neuro-symbolic AI often consider specifically-tailo…
Probabilistic ProgrammingDetecting and Quantifying Malicious Activity with Simulation-based Inference
We propose the use of probabilistic programming techniques to tackle the malicious user identification problem in a recommendation algorithm. Probabilistic programming provides numerous advantages over other techniques, …
Probabilistic ProgrammingUser IdentificationSMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation
We introduce SMProbLog, a generalization of the probabilistic logic programming language ProbLog. A ProbLog program defines a distribution over logic programs by specifying for each clause the probability that it belongs…
Probabilistic ProgrammingUnifying AI Algorithms with Probabilistic Programming using Implicitly Defined Representations
We introduce Scruff, a new framework for developing AI systems using probabilistic programming. Scruff enables a variety of representations to be included, such as code with stochastic choices, neural networks, different…
Probabilistic ProgrammingEinSteinVI: General and Integrated Stein Variational Inference
Stein variational inference is a technique for approximate Bayesian inference that has recently gained popularity because it combines the scalability of variational inference (VI) with the flexibility of non-parametric i…
Bayesian InferenceProbabilistic ProgrammingVariational InferenceGaussian Processes to speed up MCMC with automatic exploratory-exploitation effect
We present a two-stage Metropolis-Hastings algorithm for sampling probabilistic models, whose log-likelihood is computationally expensive to evaluate, by using a surrogate Gaussian Process (GP) model. The key feature of …
Gaussian ProcessesProbabilistic ProgrammingProceedings 37th International Conference on Logic Programming (Technical Communications)
ICLP is the premier international event for presenting research in logic programming. Contributions to ICLP 2021 were sought in all areas of logic programming, including but not limited to: Foundations: Semantics, Formal…
Automated Theorem ProvingData IntegrationManagementProbabilistic ProgrammingAddressing the IEEE AV Test Challenge with Scenic and VerifAI
This paper summarizes our formal approach to testing autonomous vehicles (AVs) in simulation for the IEEE AV Test Challenge. We demonstrate a systematic testing framework leveraging our previous work on formally-driven s…
Autonomous VehiclesProbabilistic ProgrammingBob and Alice Go to a Bar: Reasoning About Future With Probabilistic Programs
It is well known that reinforcement learning can be cast as inference in an appropriate probabilistic model. However, this commonly involves introducing a distribution over agent trajectories with probabilities proportio…
Bayesian InferenceProbabilistic Programmingreinforcement-learningReinforcement Learning+1Pixyz: a Python library for developing deep generative models
With the recent rapid progress in the study of deep generative models (DGMs), there is a need for a framework that can implement them in a simple and generic way. In this research, we focus on two features of DGMs: (1) d…
Probabilistic ProgrammingUnifying incidence and prevalence under a time-varying general branching process
Renewal equations are a popular approach used in modelling the number of new infections, i.e., incidence, in an outbreak. We develop a stochastic model of an outbreak based on a time-varying variant of the Crump-Mode-Jag…
EpidemiologyProbabilistic ProgrammingSupervised Bayesian Specification Inference from Demonstrations
When observing task demonstrations, human apprentices are able to identify whether a given task is executed correctly long before they gain expertise in actually performing that task. Prior research into learning from de…
Probabilistic ProgrammingNonparametric Hamiltonian Monte Carlo
Probabilistic programming uses programs to express generative models whose posterior probability is then computed by built-in inference engines. A challenging goal is to develop general purpose inference algorithms that …
Probabilistic ProgrammingExpectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently
We show that the standard computational pipeline of probabilistic programming systems (PPSs) can be inefficient for estimating expectations and introduce the concept of expectation programming to address this. In expecta…
Probabilistic ProgrammingHow To Train Your Program: a Probabilistic Programming Pattern for Bayesian Learning From Data
We present a Bayesian approach to machine learning with probabilistic programs. In our approach, training on available data is implemented as inference on a hierarchical model. The posterior distribution of model paramet…
Probabilistic ProgrammingProbabilistic Programming Bots in Intuitive Physics Game Play
Recent findings suggest that humans deploy cognitive mechanism of physics simulation engines to simulate the physics of objects. We propose a framework for bots to deploy probabilistic programming tools for interacting w…
Probabilistic ProgrammingD3p -- A Python Package for Differentially-Private Probabilistic Programming
We present d3p, a software package designed to help fielding runtime efficient widely-applicable Bayesian inference under differential privacy guarantees. d3p achieves general applicability to a wide range of probabilist…
Bayesian InferenceProbabilistic ProgrammingregressionVariational InferenceRecSim NG: Toward Principled Uncertainty Modeling for Recommender Ecosystems
The development of recommender systems that optimize multi-turn interaction with users, and model the interactions of different agents (e.g., users, content providers, vendors) in the recommender ecosystem have drawn inc…
counterfactualProbabilistic ProgrammingRecommendation SystemsMeta-Learning an Inference Algorithm for Probabilistic Programs
We present a meta-algorithm for learning a posterior-inference algorithm for restricted probabilistic programs. Our meta-algorithm takes a training set of probabilistic programs that describe models with observations, an…
Meta-LearningProbabilistic ProgrammingCompositional Semantics for Probabilistic Programs with Exact Conditioning
We define a probabilistic programming language for Gaussian random variables with a first-class exact conditioning construct. We give operational, denotational and equational semantics for this language, establishing con…
Probabilistic Programming