Papers Probabilistic Programming
“Probabilistic Programming” 태그가 달린 논문 273편 · 필터 해제
Rotation Invariant Householder Parameterization for Bayesian PCA
We consider probabilistic PCA and related factor models from a Bayesian perspective. These models are in general not identifiable as the likelihood has a rotational symmetry. This gives rise to complicated posterior dist…
Probabilistic ProgrammingModular Deep Probabilistic Programming
Modularity is a key feature of deep learning libraries but has not been fully exploited for probabilistic programming. We propose to improve modularity of probabilistic programming language by offering not only plain pro…
Probabilistic ProgrammingVariational InferenceA Bayesian Monte Carlo approach for predicting the spread of infectious diseases
In this paper, a simple yet interpretable, probabilistic model is proposed for the prediction of reported case counts of infectious diseases. A spatio-temporal kernel is derived from training data to capture the typical …
Bayesian InferenceDisease PredictionEpidemiologyMultivariate Time Series Forecasting+1Reversible Jump Probabilistic Programming
In this paper we present a method for automatically deriving a Reversible Jump Markov chain Monte Carlo sampler from probabilistic programs that specify the target and proposal distributions. The main challenge in automa…
Bayesian InferenceCode GenerationProbabilistic ProgrammingThe Random Conditional Distribution for Higher-Order Probabilistic Inference
The need to condition distributional properties such as expectation, variance, and entropy arises in algorithmic fairness, model simplification, robustness and many other areas. At face value however, distributional prop…
FairnessProbabilistic ProgrammingApplying Probabilistic Programming to Affective Computing
Affective Computing is a rapidly growing field spurred by advancements in artificial intelligence, but often, held back by the inability to translate psychological theories of emotion into tractable computational models.…
Probabilistic ProgrammingEfficient Search-Based Weighted Model Integration
Weighted model integration (WMI) extends Weighted model counting (WMC) to the integration of functions over mixed discrete-continuous domains. It has shown tremendous promise for solving inference problems in graphical m…
Computational EfficiencymodelProbabilistic ProgrammingLF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
We develop a new Low-level, First-order Probabilistic Programming Language (LF-PPL) suited for models containing a mix of continuous, discrete, and/or piecewise-continuous variables. The key success of this language and …
Probabilistic ProgrammingTensor Variable Elimination for Plated Factor Graphs
A wide class of machine learning algorithms can be reduced to variable elimination on factor graphs. While factor graphs provide a unifying notation for these algorithms, they do not provide a compact way to express repe…
Music ModelingProbabilistic ProgrammingSentiment Analysistensor algebraProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language
Optimizing an expensive-to-query function is a common task in science and engineering, where it is beneficial to keep the number of queries to a minimum. A popular strategy is Bayesian optimization (BO), which leverages …
Bayesian OptimizationGaussian ProcessesProbabilistic ProgrammingDoubly Bayesian Optimization
Probabilistic programming systems enable users to encode model structure and naturally reason about uncertainties, which can be leveraged towards improved Bayesian optimization (BO) methods. Here we present a probabilist…
Bayesian OptimizationProbabilistic ProgrammingBayesian Layers: A Module for Neural Network Uncertainty
We describe Bayesian Layers, a module designed for fast experimentation with neural network uncertainty. It extends neural network libraries with drop-in replacements for common layers. This enables composition via a uni…
Gaussian ProcessesMachine TranslationProbabilistic ProgrammingTranslationNested Reasoning About Autonomous Agents Using Probabilistic Programs
As autonomous agents become more ubiquitous, they will eventually have to reason about the plans of other agents, which is known as theory of mind reasoning. We develop a planning-as-inference framework in which agents p…
Probabilistic ProgrammingBayesian Inference of Temporal Task Specifications 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…
Bayesian InferenceProbabilistic ProgrammingJoint Mapping and Calibration via Differentiable Sensor Fusion
We leverage automatic differentiation (AD) and probabilistic programming to develop an end-to-end optimization algorithm for batch triangulation of a large number of unknown objects. Given noisy detections extracted from…
ClusteringProbabilistic ProgrammingSensor FusionVariational InferenceEffect Handling for Composable Program Transformations in Edward2
Algebraic effects and handlers have emerged in the programming languages community as a convenient, modular abstraction for controlling computational effects. They have found several applications including concurrent pro…
Probabilistic ProgrammingA Factor Graph Approach to Automated Design of Bayesian Signal Processing Algorithms
The benefits of automating design cycles for Bayesian inference-based algorithms are becoming increasingly recognized by the machine learning community. As a result, interest in probabilistic programming frameworks has m…
Bayesian Inferenceparameter estimationProbabilistic ProgrammingState Space Models+1Simple, Distributed, and Accelerated Probabilistic Programming
We describe a simple, low-level approach for embedding probabilistic programming in a deep learning ecosystem. In particular, we distill probabilistic programming down to a single abstraction---the random variable. Our l…
GPUProbabilistic ProgrammingProbabilistic Programming with Densities in SlicStan: Efficient, Flexible and Deterministic
Stan is a probabilistic programming language that has been increasingly used for real-world scalable projects. However, to make practical inference possible, the language sacrifices some of its usability by adopting a bl…
Probabilistic ProgrammingPyro: Deep Universal Probabilistic Programming
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research. To scale to large datasets and high-dimensional models, Pyro uses stochastic variati…
GPUProbabilistic ProgrammingVariational Inference