Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
Authors: José Miguel Hernández-Lobato, Matthew W Hoffman, Zoubin Ghahramani
NeurIPS 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate PES in both synthetic and real-world applications, including optimization problems in machine learning, finance, biotechnology, and robotics. We show that the increased accuracy of PES leads to significant gains in optimization performance. |
| Researcher Affiliation | Academia | Jos e Miguel Hern andez-Lobato jmh233@cam.ac.uk University of Cambridge |
| Pseudocode | Yes | Algorithm 1 Generic Bayesian optimization |
| Open Source Code | Yes | The code for all these operations is publicly available at http://jmhl.org. |
| Open Datasets | Yes | The first one (NNet) returns the predictive accuracy of a neural network on a random train/test partition of the Boston Housing dataset [3]. [3] K. Bache and M. Lichman. UCI machine learning repository, 2013. |
| Dataset Splits | No | The paper mentions 'random train/test partition' but does not specify exact percentages or a validation split. It uses 'validation' in the context of mathematical terms but not for dataset splitting. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependency details with version numbers. |
| Experiment Setup | Yes | In our experiments, we use Gaussian process priors for f with squared-exponential kernels k(x, x ) = γ2 exp{ 0.5 P i(xi x i)2/ℓ2 i }. The corresponding spectral density is zero-mean Gaussian with covariance given by diag([ℓ 2 i ]) and normalizing constant α = γ2. The model hyperparameters are {γ, ℓ1, . . . , ℓd, σ2}. We use broad, uninformative Gamma hyperpriors. |