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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 …
Yicheng Luo
,
Antonio Filieri
,
Yuan Zhou
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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 …
Viacheslav Borovitskiy
,
Alexander Terenin
,
Peter Mostowsky
,
Marc Deisenroth
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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 …
Jean Kaddour
,
Steindór Sæmundsson
,
Marc Deisenroth
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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 …
Alexander Terenin
,
Måns Magnusson
,
Leif Jonsson
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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 …
James Wilson
,
Viacheslav Borovitskiy
,
Alexander Terenin
,
Peter Mostowsky
,
Marc Deisenroth
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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 …
Samuel Cohen
,
Rendani Mbuvha
,
Tshilidzi Marwala
,
Marc Deisenroth
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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 …
Martin Jørgensen
,
Marc Deisenroth
,
Hugh Salimbeni
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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. …
Steindór Sæmundsson
,
Alexander Terenin
,
Katja Hofmann
,
Marc Deisenroth
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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 …
Alexander Terenin
,
Daniel Simpson
,
David Draper
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Disentangled Skill Embeddings for Reinforcement Learning
Janith C. Petangoda
,
Sergio Pascoal-Diaz
,
Vincent Adam
,
Peter Vrancx
,
Jordi Grau-Moya
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