2 citations · 6 across the 6 of their papers we have counts for
5 papers · 1 filter
Thin and Deep Gaussian Processes
Daniel Augusto de Souza, Alexander Nikitin, ST John +6
Gaussian processes (GPs) can provide a principled approach to uncertainty quantification with easy-to-interpret kernel hyperparameters, such as the lengthscale, which controls the…
Actually Sparse Variational Gaussian Processes
Harry Jake Cunningham, Daniel Augusto de Souza, So Takao +2
Gaussian processes (GPs) are typically criticised for their unfavourable scaling in both computational and memory requirements. For large datasets, sparse GPs reduce these demands…
Parallel MCMC Without Embarrassing Failures
Daniel Augusto de Souza, Diego Mesquita, Samuel Kaski +1
Embarrassingly parallel Markov Chain Monte Carlo (MCMC) exploits parallel computing to scale Bayesian inference to large datasets by using a two-step approach. First, MCMC is run i…
No-PASt-BO: Normalized Portfolio Allocation Strategy for Bayesian Optimization
Thiago de P. Vasconcelos, Daniel A. R. M. A. de Souza, César L. C. Mattos +1
Bayesian Optimization (BO) is a framework for black-box optimization that is especially suitable for expensive cost functions. Among the main parts of a BO algorithm, the acquisiti…
Learning GPLVM with arbitrary kernels using the unscented transformation
Daniel Augusto R. M. A. de Souza, Diego Mesquita, César Lincoln C. Mattos +1
Gaussian Process Latent Variable Model (GPLVM) is a flexible framework to handle uncertain inputs in Gaussian Processes (GPs) and incorporate GPs as components of larger graphical…