Robust Gaussian process regression based on iterative trimming

Robust estimation for the Cox regression model based on trimming.

We propose a robust Cox regression model with outliers. The model is fit by trimming the smallest contributions to the partial likelihood. To do so, we implement a Metropolis-type maximization routine, and show its convergence to a global optimum. We discuss global robustness properties of the approach, which is illustrated and compared through simulations. We finally fit the model on an origin...

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...

Robust clusterwise linear regression through trimming

The presence of clusters in a data set is sometimes due to the existence of certain relations among the measured variables which vary depending on some hidden factors. In these cases, observations could be grouped in a natural way around linear and nonlinear structures and, thus, the problem of doing robust clustering around linear affine subspaces has recently been tackled through the minimiza...

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

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...