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..
Inference in Deep Gaussian Processes using Stochastic Gradient Hamiltonian Monte Carlo
Authors: Marton Havasi, José Miguel Hernández-Lobato, Juan José Murillo-Fuentes
NeurIPS 2018 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Lastly, we conduct experiments on a variety of supervised regression and classification tasks. We show empirically that our work significantly improves predictions on medium-large datasets at a lower computational cost. |
| Researcher Affiliation | Collaboration | Marton Havasi Department of Engineering University of Cambridge EMAIL Jos e Miguel Hern andez-Lobato Department of Engineering University of Cambridge, Microsoft Research, Alan Turing Institute EMAIL Juan Jos e Murillo-Fuentes Department of Signal Theory and Communications University of Sevilla EMAIL |
| Pseudocode | Yes | Algorithm 1 on the left side of Figure 3 presents the pseudocode for Moving Window MCEM. |
| Open Source Code | Yes | Our code is based on the Tensorflow [Abadi et al., 2015] computing library and it is publicly available at https://github.com/cambridge-mlg/sghmc_dgp. |
| Open Datasets | Yes | We conducted experiments2 on 9 UCI benchmark datasets ranging from small ( 500 datapoints) to large ( 500,000) for a fair comparison against the baseline. |
| Dataset Splits | No | We exercised a random 0.8-0.2 train-test split. |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments. |
| Software Dependencies | No | Our code is based on the Tensorflow [Abadi et al., 2015] computing library and it is publicly available at https://github.com/cambridge-mlg/sghmc_dgp. |
| Experiment Setup | Yes | Following Salimbeni and Deisenroth [2017], in all of the models, we set the learning rate to the default 0.01, the minibatch size to 10,000 and the number of iterations to 20,000. One iteration involves drawing a sample from the window and updating the hyperparameters by gradient descent as illustrated in Algorithm 1 in the left side of Figure 3. The depth varied from 0 hidden layers up to 4 with 10 nodes per layer. The covariance function was a standard squared exponential function with separate lengthscales per dimension. |