Robust and Conjugate Gaussian Process Regression
Authors: Matias Altamirano, Francois-Xavier Briol, Jeremias Knoblauch
ICML 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To demonstrate its strong empirical performance, we deploy RCGP for problems ranging from Bayesian optimisation to sparse variational Gaussian processes. |
| Researcher Affiliation | Academia | 1Department of Statistical Science, University College London, London, United Kingdom. Correspondence to: Matias Altamirano <matias.altamirano.22@ucl.ac.uk>, Franc ois-Xavier Briol <f.briol@ucl.ac.uk>, Jeremias Knoblauch <j.knoblauch@ucl.ac.uk>. |
| Pseudocode | No | The paper contains mathematical derivations and descriptions of the method, but no explicit section or figure labeled 'Pseudocode' or 'Algorithm', nor any structured, code-like algorithm blocks. |
| Open Source Code | Yes | The code is available at https://github.com/ maltamiranomontero/RCGP. |
| Open Datasets | Yes | Boston The dataset can be found at https://www.cs.toronto.edu/ delve/data/boston/bostonDetail.html. |
| Dataset Splits | Yes | Instead of pw(y|θ, σ2), we thus maximise the leave-one-out cross validation (LOO-CV) predictive posteriors via |
| Hardware Specification | Yes | All the experiments were running on an Apple M2 Pro CPU with 16 GB of memory. |
| Software Dependencies | No | The paper mentions software like 'GPflow' and refers to 'the official implementation provided in the paper' for m-GP, but does not provide specific version numbers for these software components or their dependencies. |
| Experiment Setup | Yes | All hyperparameters are selected via L-BFGS. |