Bayesian Optimization for Probabilistic Programs
Authors: Tom Rainforth, Tuan Anh Le, Jan-Willem van de Meent, Michael A. Osborne, Frank Wood
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We present applications of our method to a number of tasks including engineering design and parameter optimization. We first demonstrate the ability of BOPP to carry out unbounded optimization using a 1D problem with a significant prior-posterior mismatch as shown in Figure 4. Next we compare BOPP to the prominent BO packages SMAC [14], Spearmint [25] and TPE [3] on a number of classical benchmarks as shown in Figure 5. |
| Researcher Affiliation | Academia | Department of Engineering Science, University of Oxford College of Computer and Information Science, Northeastern University |
| Pseudocode | No | The paper includes a high-level algorithm overview in Figure 3 but does not provide formal pseudocode blocks or labeled algorithms. |
| Open Source Code | Yes | Code available at http://www.github.com/probprog/bopp/ Code available at http://www.github.com/probprog/deodorant/ |
| Open Datasets | No | The paper uses benchmark problems and simulations (e.g., 'Energy2D simulations', 'Hartmann 6D', 'SVM on-grid', 'LDA on-grid', 'pickover attractor') rather than traditional public datasets with explicit access information. |
| Dataset Splits | No | The paper evaluates on benchmark functions and simulations, not traditional datasets with specified train/validation/test splits. |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper mentions software like Anglican, Energy2D, Stan, Church, Venture, Web PPL, but does not provide specific version numbers for these or other software dependencies used in their experimental setup. |
| Experiment Setup | Yes | BOPP therefore employs an affine scaling to a [ 1, 1] hypercube for both the inputs and outputs of the GP. We use as a default covariance function a combination of a Mat ern3/2 and Mat ern-5/2 kernel. Inference over hyperparameters is performed using Hamiltonian Monte Carlo (HMC) [6]. r is a parameter set to 1.5re by default. ...using a variant of annealed importance sampling [19] in which lightweight Metropolis Hastings (LMH) [28] with local random-walk moves is used as the base transition kernel. |