arXiv:2605.07565v2 Announce Type: replace-cross Abstract: We study Bayesian Optimisation (BO) in settings where the objective function is influenced by uncontrollable environmental contexts governed by an unknown probability distribution. In practice, the contextual distribution must be estimated from empirical data, a process that inherently introduces distributional mismatch, producing sub-optimal results. While Distributionally Robust Optimisation (DRO) provides a framework to mitigate these risks, existing robust BO methods frequently suffer from high computational complexity, rely on disc
This paper addresses a critical challenge in real-world AI applications where environmental factors introduce uncertainty in objective functions, reflecting the ongoing push toward more robust and reliable AI systems.
Improved robust Bayesian Optimization contributes to more dependable and efficient AI deployments, particularly in dynamic environments where estimated distributions can lead to suboptimal outcomes.
This research provides a method to mitigate risks from distributional mismatch in contextual Bayesian Optimization, offering a more computationally efficient approach compared to existing methods.
- · AI researchers and developers
- · Industries deploying AI in variable environments (e.g., robotics, autonomous sys
- · AI-driven optimization platforms
- · AI solutions with high sensitivity to environmental estimation errors
- · Current computationally intensive robust BO methods
More reliable and efficient AI models capable of operating in uncertain real-world conditions.
Accelerated adoption of AI in fields requiring high robustness and adaptability to changing environments.
Enhanced trust in autonomous AI systems as their performance becomes more predictable under varying conditions.
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Read at arXiv cs.AI