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Found 24 papers, 12 papers with code
Integrated Variational Fourier Features for Fast Spatial Modelling with Gaussian Processes
Sparse variational approximations are popular methods for scaling up inference and learning in Gaussian processes to larger datasets.
Numerically Stable Sparse Gaussian Processes via Minimum Separation using Cover Trees
For low-dimensional tasks such as geospatial modeling, we propose an automated method for computing inducing points satisfying these conditions.
Clipping Loops for Sample-Efficient Dialogue Policy Optimisation
Second, in advantage clipping, we estimate and clip the advantages of useless responses and normal ones separately.
Marginalised Spectral Mixture Kernels with Nested Sampling
Gaussian Process (GPs) models are a rich distribution over functions with inductive biases controlled by a kernel function.
Marginalised Gaussian Processes with Nested Sampling
Learning occurs through the optimisation of kernel hyperparameters using the marginal likelihood as the objective.
Ensembling geophysical models with Bayesian Neural Networks
Ensembles of geophysical models improve projection accuracy and express uncertainties.
Convergence of Sparse Variational Inference in Gaussian Processes Regression
Gaussian processes are distributions over functions that are versatile and mathematically convenient priors in Bayesian modelling.
Variational Orthogonal Features
We present a construction of features for any stationary prior kernel that allow for computation of an unbiased estimator to the ELBO using $T$ Monte Carlo samples in $\mathcal{O}(\tilde{N}T+M^2T)$ and in $\mathcal{O}(\tilde{N}T+MT)$ with an additional approximation.
Approximate Inference for Fully Bayesian Gaussian Process Regression
An alternative learning procedure is to infer the posterior over hyperparameters in a hierarchical specification of GPs we call \textit{Fully Bayesian Gaussian Process Regression} (GPR).
Benchmarking the Neural Linear Model for Regression
The neural linear model is a simple adaptive Bayesian linear regression method that has recently been used in a number of problems ranging from Bayesian optimization to reinforcement learning.
Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models
As we demonstrate in our experiments, the factorisation between latent system states and transition function can lead to a miscalibrated posterior and to learning unnecessarily large noise terms.
PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos
Previously, the exploding gradient problem has been explained to be central in deep learning and model-based reinforcement learning, because it causes numerical issues and instability in optimization.
Model-based Reinforcement Learning
reinforcement-learning
+1
Non-Factorised Variational Inference in Dynamical Systems
We focus on variational inference in dynamical systems where the discrete time transition function (or evolution rule) is modelled by a Gaussian process.
Closed-form Inference and Prediction in Gaussian Process State-Space Models
We examine an analytic variational inference scheme for the Gaussian Process State Space Model (GPSSM) - a probabilistic model for system identification and time-series modelling.
Deep Convolutional Networks as shallow Gaussian Processes
For a CNN, the equivalent kernel can be computed exactly and, unlike "deep kernels", has very few parameters: only the hyperparameters of the original CNN.
Data-Efficient Reinforcement Learning in Continuous State-Action Gaussian-POMDPs
This enables data-efficient learning under significant observation noise, outperforming more naive methods such as post-hoc application of a filter to policies optimised by the original (unfiltered) PILCO algorithm.
Convolutional Gaussian Processes
We present a practical way of introducing convolutional structure into Gaussian processes, making them more suited to high-dimensional inputs like images.
Understanding Probabilistic Sparse Gaussian Process Approximations
Good sparse approximations are essential for practical inference in Gaussian Processes as the computational cost of exact methods is prohibitive for large datasets.
Data-Efficient Reinforcement Learning in Continuous-State POMDPs
We present a data-efficient reinforcement learning algorithm resistant to observation noise.
Gaussian Processes for Data-Efficient Learning in Robotics and Control
Autonomous learning has been a promising direction in control and robotics for more than a decade since data-driven learning allows to reduce the amount of engineering knowledge, which is otherwise required.
Manifold Gaussian Processes for Regression
This feature space is often learned in an unsupervised way, which might lead to data representations that are not useful for the overall regression task.
Integrated Pre-Processing for Bayesian Nonlinear System Identification with Gaussian Processes
We introduce GP-FNARX: a new model for nonlinear system identification based on a nonlinear autoregressive exogenous model (NARX) with filtered regressors (F) where the nonlinear regression problem is tackled using sparse Gaussian processes (GP).
Robust Filtering and Smoothing with Gaussian Processes
We propose a principled algorithm for robust Bayesian filtering and smoothing in nonlinear stochastic dynamic systems when both the transition function and the measurement function are described by non-parametric Gaussian process (GP) models.
