ProBO: Versatile Bayesian Optimization Using Any Probabilistic Programming Language

By Willie Neiswanger, Kirthevasan Kandasamy, Barnab'as P'oczos, Jeff Schneider, Eric P. Xing. 2019

10 citations recorded by the source corpus.

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Willie Neiswanger
2. Colin White
3. Yash Savani
4. Sam Nolen

Citations in the corpus

• BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search (2019, positive): “Following previous work (Neiswanger et al. 2019), we use the following acquisition function estimates for an input architecture a ∈ A: φTS(x) = fm̃(x), m̃ ∼ Unif (1,M) (1) φITS(x) = f̃x(x), f̃x(x) ∼ N (f̂ , σ̂2) (2) φUCB(x) = f̂ − βσ̂ (3) φEI(a) = E [1 [fm(a) > ymin] (ymin − fm(a))] (4) = ∫ ymin −∞…”