8 citations · 19 across the 5 of their papers we have counts for
6 papers
Good practices for Bayesian Optimization of high dimensional structured spaces
Eero Siivola, Javier Gonzalez, Andrei Paleyes +1
The increasing availability of structured but high dimensional data has opened new opportunities for optimization. One emerging and promising avenue is the exploration of unsupervi…
Preferential Batch Bayesian Optimization
Eero Siivola, Akash Kumar Dhaka, Michael Riis Andersen +3
Most research in Bayesian optimization (BO) has focused on \emph{direct feedback} scenarios, where one has access to exact values of some expensive-to-evaluate objective. This dire…
Active Learning for Decision-Making from Imbalanced Observational Data
Iiris Sundin, Peter Schulam, Eero Siivola +3
Machine learning can help personalized decision support by learning models to predict individual treatment effects (ITE). This work studies the reliability of prediction-based deci…
Correcting boundary over-exploration deficiencies in Bayesian optimization with virtual derivative sign observations
Eero Siivola, Aki Vehtari, Jarno Vanhatalo +2
Bayesian optimization (BO) is a global optimization strategy designed to find the minimum of an expensive black-box function, typically defined on a compact subset of $\mathcal{R}^…
Automatic monotonicity detection for Gaussian Processes
Eero Siivola, Juho Piironen, Aki Vehtari
We propose a new method for automatically detecting monotonic input-output relationships from data using Gaussian Process (GP) models with virtual derivative observations. Our resu…
Requirement verification in simulation-based automation testing
Eero Siivola, Seppo Sierla, Hannu Niemistö +2
The emergence of the Industrial Internet results in an increasing number of complicated temporal interdependencies between automation systems and the processes to be controlled. Th…