May 3, 2018 · In this work, we first present a flexible framework, denoted graph Bayesian optimization, to handle arbitrary graphs in the Bayesian optimization community. By combining …

To this end, in this work we propose a novel algorithm, which we coin COMBO for combinatorial Bayesian Opti-mization using graph representations. COMBO is specif-ically designed for …

May 15, 2021 · This work focuses on applying Bayesian optimization to optimize network structure with graph-structured inputs, and presents a flexible framework, denoted as graph Bayesian …

We propose a general algorithmic framework for Bayesian model selection. A spike-and-slab Laplacian prior is introduced to model the underlying structural assumption. Using the notion of …

In this work, we first present a flexible framework, denoted graph Bayesian optimization, to handle arbitrary graphs in the Bayesian optimization community.

Liang et al., “Bayesian optimization of functions over node subsets in graphs,” NeurIPS, 2024. Case 4: Identifying subset of important nodes • Objective: find the node subset with …

On this combinatorial graph, we propose an ARD diffusion kernel with which the GP is able to model high-order interactions between variables leading to better performance.

The Bayesian Optimization Algorithm incorporates methods for learning Bayesian networks and uses these to model the promising solutions and generate new ones. The aim of this paper is …

We address the problem of optimizing over functions defined on node subsets in a graph. The optimization of such functions is often a non-trivial task given their combinatorial, black-box …

Apr 15, 2025 · A Bayesian Optimisation graph is a graphical representation of the Bayesian Optimisation process. It consists of a set of nodes, which represent the points evaluated, and …