
Convergence of Sparse Variational Inference in Gaussian Processes Regression
Gaussian processes are distributions over functions that are versatile a...
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Variational Orthogonal Features
Sparse stochastic variational inference allows Gaussian process models t...
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Approximate Inference for Fully Bayesian Gaussian Process Regression
Learning in Gaussian Process models occurs through the adaptation of hyp...
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Benchmarking the Neural Linear Model for Regression
The neural linear model is a simple adaptive Bayesian linear regression ...
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Overcoming MeanField Approximations in Recurrent Gaussian Process Models
We identify a new variational inference scheme for dynamical systems who...
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PIPPS: Flexible ModelBased Policy Search Robust to the Curse of Chaos
Previously, the exploding gradient problem has been explained to be cent...
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NonFactorised Variational Inference in Dynamical Systems
We focus on variational inference in dynamical systems where the discret...
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Closedform Inference and Prediction in Gaussian Process StateSpace Models
We examine an analytic variational inference scheme for the Gaussian Pro...
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Deep Convolutional Networks as shallow Gaussian Processes
We show that the output of a (residual) convolutional neural network (CN...
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Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into G...
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Understanding Probabilistic Sparse Gaussian Process Approximations
Good sparse approximations are essential for practical inference in Gaus...
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DataEfficient Reinforcement Learning in ContinuousState POMDPs
We present a dataefficient reinforcement learning algorithm resistant t...
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Gaussian Processes for DataEfficient Learning in Robotics and Control
Autonomous learning has been a promising direction in control and roboti...
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Manifold Gaussian Processes for Regression
Offtheshelf Gaussian Process (GP) covariance functions encode smoothne...
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Integrated PreProcessing for Bayesian Nonlinear System Identification with Gaussian Processes
We introduce GPFNARX: a new model for nonlinear system identification b...
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Robust Filtering and Smoothing with Gaussian Processes
We propose a principled algorithm for robust Bayesian filtering and smoo...
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Additive Gaussian Processes
We introduce a Gaussian process model of functions which are additive. A...
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Carl Edward Rasmussen
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