73 citations · 131 across the 6 of their papers we have counts for
6 papers
Bayesian Optimization for Macro Placement
Changyong Oh, Roberto Bondesan, Dana Kianfar +6
Macro placement is the problem of placing memory blocks on a chip canvas. It can be formulated as a combinatorial optimization problem over sequence pairs, a representation which d…
Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel
Changyong Oh, Roberto Bondesan, Efstratios Gavves +1
In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive-to-evaluate objectives. We first introdu…
Mixed Variable Bayesian Optimization with Frequency Modulated Kernels
Changyong Oh, Efstratios Gavves, Max Welling
The sample efficiency of Bayesian optimization(BO) is often boosted by Gaussian Process(GP) surrogate models. However, on mixed variable spaces, surrogate models other than GPs are…
Radial and Directional Posteriors for Bayesian Neural Networks
Changyong Oh, Kamil Adamczewski, Mijung Park
We propose a new variational family for Bayesian neural networks. We decompose the variational posterior into two components, where the radial component captures the strength of ea…
Combinatorial Bayesian Optimization using the Graph Cartesian Product
Changyong Oh, Jakub M. Tomczak, Efstratios Gavves +1
This paper focuses on Bayesian Optimization (BO) for objectives on combinatorial search spaces, including ordinal and categorical variables. Despite the abundance of potential appl…
BOCK : Bayesian Optimization with Cylindrical Kernels
ChangYong Oh, Efstratios Gavves, Max Welling
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. In this paper…