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Found 20 papers, 9 papers with code
Reparameterized Variational Rejection Sampling
To expand the space of flexible variational families, we revisit Variational Rejection Sampling (VRS) [Grover et al., 2018], which combines a parametric proposal distribution with rejection sampling to define a rich non-parametric family of distributions that explicitly utilizes the known target distribution.
Bayesian Variable Selection in a Million Dimensions
Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty.
Surrogate Likelihoods for Variational Annealed Importance Sampling
Variational inference is a powerful paradigm for approximate Bayesian inference with a number of appealing properties, including support for model learning and data subsampling.
Fast Bayesian Variable Selection in Binomial and Negative Binomial Regression
Bayesian variable selection is a powerful tool for data analysis, as it offers a principled method for variable selection that accounts for prior information and uncertainty.
Scalable Cross Validation Losses for Gaussian Process Models
We introduce a simple and scalable method for training Gaussian process (GP) models that exploits cross-validation and nearest neighbor truncation.
High-Dimensional Bayesian Optimization with Sparse Axis-Aligned Subspaces
Bayesian optimization (BO) is a powerful paradigm for efficient optimization of black-box objective functions.
Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization
Matrix square roots and their inverses arise frequently in machine learning, e. g., when sampling from high-dimensional Gaussians $\mathcal{N}(\mathbf 0, \mathbf K)$ or whitening a vector $\mathbf b$ against covariance matrix $\mathbf K$.
Deep Sigma Point Processes
We introduce Deep Sigma Point Processes, a class of parametric models inspired by the compositional structure of Deep Gaussian Processes (DGPs).
Composable Effects for Flexible and Accelerated Probabilistic Programming in NumPyro
NumPyro is a lightweight library that provides an alternate NumPy backend to the Pyro probabilistic programming language with the same modeling interface, language primitives and effect handling abstractions.
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
We introduce a fully stochastic gradient based approach to Bayesian optimal experimental design (BOED).
Functional Tensors for Probabilistic Programming
It is a significant challenge to design probabilistic programming systems that can accommodate a wide variety of inference strategies within a unified framework.
Parametric Gaussian Process Regressors
In an extensive empirical comparison with a number of alternative methods for scalable GP regression, we find that the resulting predictive distributions exhibit significantly better calibrated uncertainties and higher log likelihoods--often by as much as half a nat per datapoint.
Neural Likelihoods for Multi-Output Gaussian Processes
We construct flexible likelihoods for multi-output Gaussian process models that leverage neural networks as components.
Variational Bayesian Optimal Experimental Design
Bayesian optimal experimental design (BOED) is a principled framework for making efficient use of limited experimental resources.
Tensor Variable Elimination for Plated Factor Graphs
To exploit efficient tensor algebra in graphs with plates of variables, we generalize undirected factor graphs to plated factor graphs and variable elimination to a tensor variable elimination algorithm that operates directly on plated factor graphs.
Closed Form Variational Objectives For Bayesian Neural Networks with a Single Hidden Layer
In this note we consider setups in which variational objectives for Bayesian neural networks can be computed in closed form.
Pyro: Deep Universal Probabilistic Programming
Pyro is a probabilistic programming language built on Python as a platform for developing advanced probabilistic models in AI research.
Pathwise Derivatives Beyond the Reparameterization Trick
We observe that gradients computed via the reparameterization trick are in direct correspondence with solutions of the transport equation in the formalism of optimal transport.
Pathwise Derivatives for Multivariate Distributions
We exploit the link between the transport equation and derivatives of expectations to construct efficient pathwise gradient estimators for multivariate distributions.
Event Extraction Using Distant Supervision
We present a new publicly available dataset and event extraction task in the plane crash domain based on Wikipedia infoboxes and newswire text.
