Bayesian Optimization of Function Networks: Supplementary Material
–Neural Information Processing Systems
In this section, we provide a formal statement and proof of Proposition 1. We begin by proving the following auxiliary result. We are now in position to show Proposition 1, which can be seen as a simple generalization of Theorem 1 in Balandat et al. (2020). R, k = 1,..., K, are Lipschitz continuous. R R, k = 1,..., K, given by f The desired result is now a direct consequence of Proposition 2 in the supplement of Balandat et al. (2020), which is in turn a consequence of Theorem 2.3 in Homem-de Mello (2008).
Neural Information Processing Systems
May-29-2025, 05:49:39 GMT
- Country:
- Asia > China (0.14)
- North America > United States
- North Carolina (0.14)
- Genre:
- Research Report (0.46)
- Industry:
- Technology: