Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs

Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs

Title:Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs ... Abstract:Bayesian Optimization (BO) is a sample-efficient ...

Bayesian Optimization (BO) is a sample-efficient optimization algorithm widely employed across various applications. In some challenging BO tasks, input ...

Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs

In this work, we propose an Arbitrary Input uncertainty Robust Bayesian Optimization algorithm. (AIRBO). This algorithm can directly model the uncertain ...

Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs

The input uncertainty can follow arbitrary complex distribution. • Assume that we can samples from input distribution, which can be done via statistical ...

Efficient robust Bayesian optimization for arbitrary uncertain inputs

In some challenging BO tasks, input uncertainty arises due to the inevitable randomness in the optimization process, such as machining errors, ...

[PDF] Bayesian optimisation under uncertain inputs | Semantic Scholar

Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs · Lin YangJunlong LyuWenlong LyuZhitang Chen. Computer Science, Engineering. NeurIPS. 2023.

huawei-noah/HEBO: Bayesian optimisation ... - GitHub

Codebase associated to: Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs. Abstract. Bayesian Optimization (BO) is a sample-efficient ...

Noisy-Input Entropy Search for Efficient Robust Bayesian Optimization

While BO is intrinsically robust to noisy evaluations of the objective function, standard approaches do not consider the case of uncertainty about the input ...

[PDF] Bayesian Optimization Under Uncertainty - Semantic Scholar

... variable types. Expand. Add to Library. Alert. 2 Excerpts. Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs · Lin YangJunlong Lyu ...

‪Lyu Junlong‬ - ‪Google Scholar‬

2024. Efficient robust Bayesian optimization for arbitrary uncertain inputs. L Yang, J Lyu, W Lyu, Z Chen. Advances in Neural Information Processing Systems 36 ...

Junlong Lyu - CatalyzeX

Efficient Robust Bayesian Optimization for Arbitrary Uncertain Inputs. View ... robust optimum that performs consistently well under arbitrary input uncertainty ...

Robust Entropy Search for Safe Efficient Bayesian Optimization - arXiv

Efficient Robust Bayesian Optimization for Arbitrary Uncertain inputs. In Advances in Neural Information Processing Systems, 2023. Zhu et al ...

Probabilistically Robust Bayesian Optimization for Data-Driven ...

We first present a method for emulating the unknown plant dynamics using a Gaussian process (GP) model learned from input-output data. By running closed-loop ...

Efficient Distributionally Robust Bayesian Optimization with Worst ...

We develop a fast approximation of the worst-case expected value based on the notion of worst-case sensitivity that caters to arbitrary convex distribution ...

Robust Bayesian optimization for flexibility analysis of expensive ...

Specifically, flexibility analysis allows the control inputs to be optimally adjusted to the realization of the uncertain parameters, while traditional robust ...

Adversarially robust Bayesian optimization for efficient auto‐tuning ...

ARBO relies on a Gaussian process model that jointly describes the effect of the tuning parameters and uncertainties on the closed-loop ...

Bayesian Optimization Under Uncertainty

In this paper, we propose a Bayesian methodology to efficiently solve a class of robust optimization problems that arise in engineering design under uncertainty ...

Adversarially robust Bayesian optimization for efficient auto‐tuning ...

... parameters and uncertainties on the closed‐loop performance. From this joint Gaussian process model, ARBO uses an alternating confidence ...

Robust Entropy Search for Safe Efficient Bayesian Optimization

[2020], who treat the related problem of mean-case robustness against input noise by an information-theoretic approach. Proceedings of the 40th Conference on ...

Distributionally Robust Bayesian Optimization with φ φ -divergences

The study of robustness has received much attention due to its inevitability in data-driven settings where many systems face uncertainty.