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..
Orthogonally Decoupled Variational Gaussian Processes
Authors: Hugh Salimbeni, Ching-An Cheng, Byron Boots, Marc Deisenroth
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirically, our algorithm demonstrates significantly faster convergence in multiple experiments. We empirically assess the performance of our algorithm in multiple regression and classification tasks. |
| Researcher Affiliation | Academia | Hugh Salimbeni Imperial College London EMAIL Ching-An Cheng Georgia Institute of Technology EMAIL Byron Boots Georgia Institute of Technology EMAIL Marc Deisenroth Imperial College London EMAIL |
| Pseudocode | No | The paper describes update rules and mathematical formulations but does not contain explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code and datasets are publicly available. https://github.com/hughsalimbeni/orth_decoupled_var_gps https://github.com/hughsalimbeni/bayesian_benchmarks |
| Open Datasets | Yes | Our code and datasets are publicly available. https://github.com/hughsalimbeni/bayesian_benchmarks. We set |γ| = |β| = 500 for all bases and conducted experiments on 3droad dataset (N = 434874, D = 3) for regression with a Gaussian likelihood and ringnorm data (N = 7400, D = 21) for classification with a Bernoulli likelihood. |
| Dataset Splits | No | The paper does not explicitly provide specific percentages, sample counts, or a detailed methodology for training, validation, or test dataset splits in the main text. |
| Hardware Specification | Yes | We used a computer with a Tesla K40 GPU and found that, in wall-clock time, the orthogonally decoupled basis with |γ| = 3500, |β| = 1500 was equivalent to a coupled model with |β| = 2000 (about 0.7 seconds per iteration) in our tensorflow [1] implementation. |
| Software Dependencies | No | The paper mentions 'tensorflow [1] implementation' and 'Adam optimizer [16]' but does not specify their version numbers. |
| Experiment Setup | Yes | We make generic choices for hyperparameters, inducing point initializations, and data processing, which are detailed in Appendix F. We set |γ| = |β| = 500 for all bases... and the orthogonally decoupled basis with |γ| = 3500, |β| = 1500 was equivalent to a coupled model with |β| = 2000. |