Name
Abstract | ||
|---|---|---|
This paper focuses on Bayesian Optimization - typically considered with continuous inputs - for discrete search input spaces, including integer, categorical or graph structured input variables. In Gaussian process-based Bayesian Optimization a problem arises, as it is not straightforward to define a proper kernel on discrete input structures, where no natural notion of smoothness or similarity could be provided. We propose COMBO, a method that represents values of discrete variables as vertices of a graph and then use the diffusion kernel on that graph. As the graph size explodes with the number of categorical variables and categories, we propose the graph Cartesian product to decompose the graph into smaller sub-graphs, enabling kernel computation in linear time with respect to the number of input variables. Moreover, in our formulation we learn a scale parameter per subgraph. In empirical studies on four discrete optimization problems we demonstrate that our method is on par or outperforms the state-of-the-art in discrete Bayesian optimization. |
Year | Venue | DocType |
|---|---|---|
2019 | arXiv: Machine Learning | Journal |
Volume | Citations | PageRank |
abs/1902.00448 | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| ChangYong Oh | 1 | 1 | 2.05 |
| Jakub M. Tomczak | 2 | 206 | 22.84 |
| efstratios gavves | 3 | 655 | 33.41 |
| Max Welling | 4 | 4875 | 550.34 |