Name
Abstract | ||
|---|---|---|
We propose a practical Bayesian optimization method over sets, to minimize a black-box function that takes a set as a single input. Because set inputs are permutation-invariant, traditional Gaussian process-based Bayesian optimization strategies which assume vector inputs can fall short. To address this, we develop a Bayesian optimization method with set kernel that is used to build surrogate functions. This kernel accumulates similarity over set elements to enforce permutation-invariance, but this comes at a greater computational cost. To reduce this burden, we propose two key components: (i) a more efficient approximate set kernel which is still positive-definite and is an unbiased estimator of the true set kernel with upper-bounded variance in terms of the number of subsamples, (ii) a constrained acquisition function optimization over sets, which uses symmetry of the feasible region that defines a set input. Finally, we present several numerical experiments which demonstrate that our method outperforms other methods. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1007/s10994-021-05949-0 | MACHINE LEARNING |
Keywords | DocType | Volume |
Global optimization, Bayesian optimization, Set optimization | Journal | 110 |
Issue | ISSN | Citations |
5 | 0885-6125 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jungtaek Kim | 1 | 8 | 5.33 |
| Michael McCourt | 2 | 27 | 4.87 |
| Tackgeun You | 3 | 137 | 4.90 |
| Saehoon Kim | 4 | 5 | 1.12 |
| Seungjin Choi | 5 | 1444 | 133.30 |