STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian Optimization
TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function values.
ProofPaper ↗
Key points
- This paper asks whether a trust-region BO method can be extended to handle noisy evaluations while being competitive in high dimension.
- We first generalize TREGO into a trust-region framework for noisy BO and extend its convergence analysis to noisy function estimates that are sufficiently accurate with sufficiently high probability.
- We then propose STREGO, a scalable implementation of this framework.
- STREGO is compared with TREGO, TURBO, BAXUS and NSMA-BO on synthetic and real-world problems of dimension $4$ to $124$, with and without noise.
Sources (1)
- [1]STREGO: A Scalable Implementation of a Trust-Region Framework for Noisy Bayesian OptimizationarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 9, 09:29 PM
TREGO, a trust-region Bayesian optimization (BO) method for expensive blackbox problems, has convergence guarantees, but only for exact function values.
This paper asks whether a trust-region BO method can be extended to handle noisy evaluations while being competitive in high dimension.
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