Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel methods like Support Vector Machines may offer good predictive performances but do not intrinsically provide uncertainty estimates. In contrast, probabilistic kernel methods like Gaussian Processes offer uncertainty estimates in addition to good predictive performance but fall short in terms of scalability. While the scalability of Gaussian processes can be improved using sparse inducing point approximations, the selection of these inducing points remains challenging. We explore different techniques for selecting inducing points on discrete domains, including greedy...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
Variational approximations to Gaussian processes (GPs) typically use a small set of inducing points ...
Statistical inference for functions is an important topic for regression and classification problems...
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation ...
Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent...
We explore ways to scale Gaussian processes (GP) to large datasets. Two methods with different theor...
We address the limitations of Gaussian processes for multiclass classification in the setting where ...
10 pagesApproximations to Gaussian processes based on inducing variables, combined with variational ...
Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging p...
This work brings together two powerful concepts in Gaussian processes: the variational approach to s...
International audienceThis work brings together two powerful concepts in Gaussian processes: the var...
Sparse Gaussian processes and various extensions thereof are enabled through inducing points, that ...
We present a general inference framework for inter-domain Gaussian Processes (GPs) and focus on its ...
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...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
Variational approximations to Gaussian processes (GPs) typically use a small set of inducing points ...
Statistical inference for functions is an important topic for regression and classification problems...
A Gaussian Process (GP) is a prominent mathematical framework for stochastic function approximation ...
Gaussian Process (GP) inference is a probabilistic kernel method where the GP is treated as a latent...
We explore ways to scale Gaussian processes (GP) to large datasets. Two methods with different theor...
We address the limitations of Gaussian processes for multiclass classification in the setting where ...
10 pagesApproximations to Gaussian processes based on inducing variables, combined with variational ...
Approximate Bayesian inference methods that scale to very large datasets are crucial in leveraging p...
This work brings together two powerful concepts in Gaussian processes: the variational approach to s...
International audienceThis work brings together two powerful concepts in Gaussian processes: the var...
Sparse Gaussian processes and various extensions thereof are enabled through inducing points, that ...
We present a general inference framework for inter-domain Gaussian Processes (GPs) and focus on its ...
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...
Gaussian processes (GPs) are natural generalisations of multivariate Gaussian random variables to in...
Variational approximations to Gaussian processes (GPs) typically use a small set of inducing points ...
Statistical inference for functions is an important topic for regression and classification problems...