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 [1].
Preconditioning for Scalable Gaussian Process Hyperparameter Optimization
Authors: Jonathan Wenger, Geoff Pleiss, Philipp Hennig, John Cunningham, Jacob Gardner
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our theoretical results enable provably efficient optimization of kernel hyperparameters, which we validate empirically on large-scale benchmark problems. There our approach accelerates training by up to an order of magnitude. 5. Experiments We validate our theoretical findings empirically via GP hyperparameter optimization on synthetic and benchmark datasets with and without preconditioning. |
| Researcher Affiliation | Academia | 1University of Tübingen 2Max Planck Institute for Intelligent Systems, Tübingen 3Columbia University 4University of Pennsylvania. Correspondence to: Jonathan Wenger <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: log-Marginal Likelihood. Algorithm 2: Derivative of the log-Marginal Likelihood. |
| Open Source Code | Yes | An implementation of our method is available as part of GPYTORCH (Gardner et al., 2018). github.com/cornellius-gp/gpytorch |
| Open Datasets | Yes | We consider a one-dimensional synthetic dataset of n = 10,000 iid standard normal samples, as well as a range of UCI datasets (Dua & Graff, 2017) with training set sizes ranging from n = 12,449 to 326,155 (see Table 2). |
| Dataset Splits | Yes | Hyperparameters were optimized with L-BFGS using an Armijo-Wolfe line search and early stopping via a validation set. |
| Hardware Specification | Yes | All experiments were performed on single NVIDIA GPUs, a Ge Force RTX 2080 and Titan RTX, respectively. |
| Software Dependencies | No | The paper mentions GPYTORCH, L-BFGS, and Adam but does not provide specific version numbers for these software components. For example, "An implementation of our method is available as part of GPYTORCH (Gardner et al., 2018)" does not specify the GPYTORCH version. |
| Experiment Setup | Yes | We perform GP regression using an RBF and Matérn( 3/2) kernel with output scale o, lengthscales lj one per input dimension and noise σ2. Hyperparameters were optimized with L-BFGS using an Armijo-Wolfe line search and early stopping via a validation set. We use a partial Cholesky preconditioner throughout. |