# Hypervolume-based Multi-objective Bayesian Optimization with Student-t Processes

arXiv: https://arxiv.org/abs/1612.00393 · HTML: https://mlsanity.com/paper/1612.00393/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2016.

## Built on by (most cited first, 1 of 1)

- [Gearing Gaussian process modeling and sequential design towards stochastic simulators](https://mlsanity.com/paper/2412.07306/) (2024)

## Abstract (arXiv)

Student-t 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-t 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-t 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.