Search Results for author: Changyong Oh

Found 6 papers, 3 papers with code

Batch Bayesian Optimization on Permutations using Acquisition Weighted Kernels

no code implementations26 Feb 2021 Changyong Oh, Roberto Bondesan, Efstratios Gavves, Max Welling

In this work we propose a batch Bayesian optimization method for combinatorial problems on permutations, which is well suited for expensive cost functions on permutations.

Mixed Variable Bayesian Optimization with Frequency Modulated Kernels

no code implementations25 Feb 2021 Changyong Oh, Efstratios Gavves, Max Welling

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.

Hyperparameter Optimization

Radial and Directional Posteriors for Bayesian Neural Networks

2 code implementations7 Feb 2019 Changyong Oh, Kamil Adamczewski, Mijung Park

We propose a new variational family for Bayesian neural networks.

Combinatorial Bayesian Optimization using the Graph Cartesian Product

1 code implementation NeurIPS 2019 Changyong Oh, Jakub M. Tomczak, Efstratios Gavves, Max Welling

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.

Neural Architecture Search Variable Selection

BOCK : Bayesian Optimization with Cylindrical Kernels

1 code implementation ICML 2018 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.

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