Robust Gaussian process regression with a bias model

Can Gaussian Process Regression Be Made Robust Against Model Mismatch?

Learning curves for Gaussian process (GP) regression can be strongly affected by a mismatch between the ‘student’ model and the ‘teacher’ (true data generation process), exhibiting e.g. multiple overfitting maxima and logarithmically slow learning. I investigate whether GPs can be made robust against such effects by adapting student model hyperparameters to maximize the evidence (data likelihoo...

Robust Gaussian Process Regression with a Student-t Likelihood

This paper considers the robust and efficient implementation of Gaussian process regression with a Student-t observation model, which has a non-log-concave likelihood. The challenge with the Student-t model is the analytically intractable inference which is why several approximative methods have been proposed. Expectation propagation (EP) has been found to be a very accurate method in many empi...

Approximate Inference for Robust Gaussian Process Regression

Gaussian process (GP) priors have been successfully used in non-parametric Bayesian regression and classification models. Inference can be performed analytically only for the regression model with Gaussian noise. For all other likelihood models inference is intractable and various approximation techniques have been proposed. In recent years expectation-propagation (EP) has been developed as a g...

Model Learning with Local Gaussian Process Regression

Precise models of the robot inverse dynamics allow the design of significantly more accurate, energy-efficient and more compliant robot control. However, in some cases the accuracy of rigidbody models does not suffice for sound control performance due to unmodeled nonlinearities arising from hydraulic cable dynamics, complex friction or actuator dynamics. In such cases, estimating the inverse d...

Gaussian process models for robust regression, classification, and reinforcement learning