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Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels

Dynamic time warping (DTW) is a useful method for aligning, comparing and combining time series, but it requires them to live in …

Learning physically structured representations of dynamical systems that include contact between different objects is an important …

Gaussian processes are a versatile framework for learning unknown functions in a manner that permits one to utilize prior information …

Gaussian processes are an effective model class for learning unknown functions, particularly in settings where accurately representing …

Data-efficient learning algorithms are essential in many practical applications where data collection is expensive, e.g., in robotics …

Gaussian processes are the gold standard for many real-world modeling problems, especially in cases where a model’s success …

We present a Bayesian non-parametric way of inferring stochastic differential equations for both regression tasks and continuous-time …

Learning workable representations of dynamical systems is becoming an increasingly important problem in a number of application areas. …

Gaussian processes are nonparametric Bayesian models that have been applied to regression and classification problems. One of the …