When Gaussian process meets big data: A review of scalable GPs
The vast quantity of information brought by big data as well as the evolving computer
hardware encourages success stories in the machine learning community. In the …
hardware encourages success stories in the machine learning community. In the …
Rethinking Bayesian learning for data analysis: The art of prior and inference in sparsity-aware modeling
Sparse modeling for signal processing and machine learning, in general, has been at the
focus of scientific research for over two decades. Among others, supervised sparsity-aware …
focus of scientific research for over two decades. Among others, supervised sparsity-aware …
Simulation intelligence: Towards a new generation of scientific methods
The original" Seven Motifs" set forth a roadmap of essential methods for the field of scientific
computing, where a motif is an algorithmic method that captures a pattern of computation …
computing, where a motif is an algorithmic method that captures a pattern of computation …
Functional variational Bayesian neural networks
Variational Bayesian neural networks (BNNs) perform variational inference over weights, but
it is difficult to specify meaningful priors and approximate posteriors in a high-dimensional …
it is difficult to specify meaningful priors and approximate posteriors in a high-dimensional …
Exact Gaussian processes on a million data points
Gaussian processes (GPs) are flexible non-parametric models, with a capacity that grows
with the available data. However, computational constraints with standard inference …
with the available data. However, computational constraints with standard inference …
Kernel methods through the roof: handling billions of points efficiently
Kernel methods provide an elegant and principled approach to nonparametric learning, but
so far could hardly be used in large scale problems, since naïve implementations scale …
so far could hardly be used in large scale problems, since naïve implementations scale …
Hilbert space methods for reduced-rank Gaussian process regression
This paper proposes a novel scheme for reduced-rank Gaussian process regression. The
method is based on an approximate series expansion of the covariance function in terms of …
method is based on an approximate series expansion of the covariance function in terms of …
End-to-end meta-bayesian optimisation with transformer neural processes
Meta-Bayesian optimisation (meta-BO) aims to improve the sample efficiency of Bayesian
optimisation by leveraging data from related tasks. While previous methods successfully …
optimisation by leveraging data from related tasks. While previous methods successfully …
Posterior and computational uncertainty in gaussian processes
Gaussian processes scale prohibitively with the size of the dataset. In response, many
approximation methods have been developed, which inevitably introduce approximation …
approximation methods have been developed, which inevitably introduce approximation …
Permutation search of tensor network structures via local sampling
Recent works put much effort into tensor network structure search (TN-SS), aiming to select
suitable tensor network (TN) structures, involving the TN-ranks, formats, and so on, for the …
suitable tensor network (TN) structures, involving the TN-ranks, formats, and so on, for the …