Decoupled Sparse Gaussian Processes Components]{Decoupled Sparse Gaussian Processes Components : Separating Decision Making from Data Manifold Fitting
We propose a decoupling in Reproducing Kernel Hilbert Space of the parametric and non-parametric components of Sparse Gaussian Processes. We demonstrate that this decoupling results in a set of inducing points concentrated around the decision boundaries, respectively one that fits the data manifold in terms of variance. Moreover, we make connections between Bayesian Kernel Ridge Regression and Sparse Gaussian Processes in posterior function space. Inspired from this equivalence, we introduce a inducing points' locations linked parametrization of the variational variance term of inducing point values, hence resulting in models that only have variational mean parameters to optimize. We compare our model to counterparts and show improvements at testing time on large scale regression and classification datasets, alongside faster convergence rates to the full Gaussian Process.
Code (0)
등록된 구현이 없습니다.
Tasks
Decision MakingGaussian ProcessesregressionSimilar Papers 제목 키워드 기반
Orthogonally Decoupled Variational Fourier Features
Sparse inducing points have long been a standard method to fit Gaussian processes to big data. In the last few years, spectral methods that exploit approximations of the covariance kernel have shown to be competitive. In…
Gaussian ProcessesOrthogonally Decoupled Variational Gaussian Processes
Gaussian processes (GPs) provide a powerful non-parametric framework for reasoning over functions. Despite appealing theory, its superlinear computational and memory complexities have presented a long-standing challenge.…
Gaussian ProcessesVariational InferenceA Decoupled Approach for Composite Sparse-plus-Smooth Penalized Optimization
We consider a linear inverse problem whose solution is expressed as a sum of two components: one smooth and the other sparse. This problem is addressed by minimizing an objective function with a least squares data-fideli…
Deep Gaussian Processes with Decoupled Inducing Inputs
Deep Gaussian Processes (DGP) are hierarchical generalizations of Gaussian Processes (GP) that have proven to work effectively on a multiple supervised regression tasks. They combine the well calibrated uncertainty estim…
Gaussian ProcessesVariational Gaussian Processes with Decoupled Conditionals
Variational Gaussian processes (GPs) approximate exact GP inference by using a small set of inducing points to form a sparse approximation of the true posterior, with the fidelity of the model increasing with additional …