activity
20182022
most citedBayesian optimisation under uncertain inputs

19 citations · 20 across the 3 of their papers we have counts for

collaborators

6 papers

cs.LG20221 cited

Batch Bayesian optimisation via density-ratio estimation with guarantees

Rafael Oliveira, Louis Tiao, Fabio Ramos

Bayesian optimisation (BO) algorithms have shown remarkable success in applications involving expensive black-box functions. Traditionally BO has been set as a sequential decision-…

cs.RO2022

Adaptive Model Predictive Control by Learning Classifiers

Rel Guzman, Rafael Oliveira, Fabio Ramos

Stochastic model predictive control has been a successful and robust control framework for many robotics tasks where the system dynamics model is slightly inaccurate or in the pres…

cs.LG2020

Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning

Anthony Tompkins, Rafael Oliveira, Fabio Ramos

We establish a general form of explicit, input-dependent, measure-valued warpings for learning nonstationary kernels. While stationary kernels are ubiquitous and simple to use, the…

cs.RO2020

DISCO: Double Likelihood-free Inference Stochastic Control

Lucas Barcelos, Rafael Oliveira, Rafael Possas +2

Accurate simulation of complex physical systems enables the development, testing, and certification of control strategies before they are deployed into the real systems. As simulat…

cs.LG201919 cited

Bayesian optimisation under uncertain inputs

Rafael Oliveira, Lionel Ott, Fabio Ramos

Bayesian optimisation (BO) has been a successful approach to optimise functions which are expensive to evaluate and whose observations are noisy. Classical BO algorithms, however,…

cs.RO2018

Learning to Race through Coordinate Descent Bayesian Optimisation

Rafael Oliveira, Fernando H. M. Rocha, Lionel Ott +3

In the automation of many kinds of processes, the observable outcome can often be described as the combined effect of an entire sequence of actions, or controls, applied throughout…