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Found 6 papers, 4 papers with code
BOCK : Bayesian Optimization with Cylindrical Kernels
A major challenge in Bayesian Optimization is the boundary issue (Swersky, 2017) where an algorithm spends too many evaluations near the boundary of its search space.
Combinatorial Bayesian Optimization using the Graph Cartesian Product
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.
Radial and Directional Posteriors for Bayesian Neural Networks
We propose a new variational family for Bayesian neural networks.
Mixed Variable Bayesian Optimization with Frequency Modulated Kernels
In experiments, we demonstrate the improved sample efficiency of GP BO using FM kernels (BO-FM). On synthetic problems and hyperparameter optimization problems, BO-FM outperforms competitors consistently.
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel
In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive-to-evaluate objectives.
Bayesian Optimization for Macro Placement
Macro placement is the problem of placing memory blocks on a chip canvas.
