2 research outputs found
Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes
Student- processes have recently been proposed as an appealing alternative
non-parameteric function prior. They feature enhanced flexibility and
predictive variance. In this work the use of Student- processes are explored
for multi-objective Bayesian optimization. In particular, an analytical
expression for the hypervolume-based probability of improvement is developed
for independent Student- process priors of the objectives. Its effectiveness
is shown on a multi-objective optimization problem which is known to be
difficult with traditional Gaussian processes.Comment: 5 pages, 3 figure