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Symbolic Parallel Adaptive Importance Sampling for Probabilistic Program Analysis
Probabilistic software analysis aims at quantifying the probability of a target event occurring during the execution of a program …
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Matérn Gaussian Processes on Riemannian Manifolds
Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing …
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Probabilistic Active Meta-Learning
Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics …
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Sparse Parallel Training of Hierarchical Dirichlet Process Topic Models
Nonparametric extensions of topic models such as Latent Dirichlet Allocation, including Hierarchical Dirichlet Process (HDP), are often …
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Efficiently Sampling Functions from Gaussian Process Posteriors
Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model’s success …
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Healing Products of Gaussian Process Experts
Gaussian processes are nonparametric Bayesian models that have been applied to regression and classification problems. One of the …
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Stochastic Differential Equations with Variational Wishart Diffusions
We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time …
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Variational Integrator Networks for Physically Structured Embeddings
Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. …
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Asynchronous Gibbs Sampling
Gibbs sampling is a Markov Chain Monte Carlo (MCMC) method often used in Bayesian learning. It is widely believed that MCMC methods are …
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Disentangled Skill Embeddings for Reinforcement Learning
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