Regression with input-dependent noise: a Gaussian process treatment
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Predictive Approaches for Choosing Hyperparameters in Gaussian Processes
Neural Computation
Neural Computation
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Flying over the reality gap: From simulated to real indoor airships
Autonomous Robots
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
Covariate Shift Adaptation by Importance Weighted Cross Validation
The Journal of Machine Learning Research
Gaussian process dynamic programming
Neurocomputing
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Gaussian processes are a powerful non-parametric framework for solving various regression problems. In this paper, we address the task of learning a Gaussian process model of non-stationary system dynamics in an online fashion. We propose an extension to previous models that can appropriately handle outdated training samples by decreasing their influence onto the predictive distribution. The resulting model estimates for each sample of the training set an individual noise level and thereby produces a mean shift towards more reliable observations. As a result, our model improves the prediction accuracy in the context of non-stationary function approximation and can furthermore detect outliers based on the resulting noise level. Our approach is easy to implement and is based upon standard Gaussian process techniques. In a real-world application where the task is to learn the system dynamics of a miniature blimp, we demonstrate that our algorithm benefits from individual noise levels and outperforms standard methods.