Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Predictive Entropy Search for Efficient Global Optimization of Black-box Functions
Authors: José Miguel Hernández-Lobato, Matthew W Hoffman, Zoubin Ghahramani
NeurIPS 2014 | Venue PDF | 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 EMAIL 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. |