We propose an efficient optimization algorithm for selecting a subset of train-ing data to induce sparsity for Gaussian process regression. The algorithm esti-mates an inducing set and the hyperparameters using a single objective, either the marginal likelihood or a variational free energy. The space and time complexity are linear in training set size, and the algorithm can be applied to large regression problems on discrete or continuous domains. Empirical evaluation shows state-of-art performance in discrete cases and competitive results in the continuous case.
This paper proposes an approach for online training of a sparse multi-output Gaussian process (GP) m...
The mixture of Gaussian Processes (MGP) is a powerful and fast developed machine learning framework....
Gaussian processes; Non-parametric regression; System identification. Abstract: We provide a method ...
We propose an efficient optimization algorithm for selecting a subset of training data to induce spa...
Abstract—We propose an efficient optimization algorithm to select a subset of training data as the i...
We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the th...
We present a new Gaussian process (GP) regression model whose covariance is parameterized by the th...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
This paper proposes an approach for online training of a sparse multi-output Gaussian process (GP) m...
The mixture of Gaussian Processes (MGP) is a powerful and fast developed machine learning framework....
Gaussian processes; Non-parametric regression; System identification. Abstract: We provide a method ...
We propose an efficient optimization algorithm for selecting a subset of training data to induce spa...
Abstract—We propose an efficient optimization algorithm to select a subset of training data as the i...
We present a new Gaussian process (GP) regression model whose co-variance is parameterized by the th...
We present a new Gaussian process (GP) regression model whose covariance is parameterized by the th...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
Most existing sparse Gaussian process (g.p.) models seek computational advantages by basing their co...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
We present a new sparse Gaussian Process (GP) model for regression. The key novel idea is to sparsif...
This paper proposes an approach for online training of a sparse multi-output Gaussian process (GP) m...
The mixture of Gaussian Processes (MGP) is a powerful and fast developed machine learning framework....
Gaussian processes; Non-parametric regression; System identification. Abstract: We provide a method ...
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