Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
Authors: Michael K. Cohen, Samuel Daulton, Michael A Osborne
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | On a classic suite of regression tasks, we compare our kernel against Matérn, sparse, and sparse variational kernels. The binary tree GP assigns the highest likelihood to the test data on a plurality of datasets, usually achieves lower mean squared error than the sparse methods, and often ties or beats the Matérn GP. |
| Researcher Affiliation | Collaboration | Michael K. Cohen University of Oxford michael.cohen@eng.ox.ax.uk Samuel Daulton University of Oxford, Meta sdaulton@meta.com Michael A. Osborne University of Oxford mosb@robots.ox.ax.uk |
| Pseudocode | Yes | Algorithm 1 Linear Transformation with SROS Linear Operator. Algorithm 2 Inverse and determinant of I+ SROS Linear Operator. |
| Open Source Code | Yes | The code is available at https://github.com/mkc1000/btgp and https://tinyurl.com/btgp-colab. |
| Open Datasets | Yes | We evaluate our method on the same open-access UCI datasets [4] as Wang et al. [25]... [4] Dheeru Dua and Casey Graff. UCI machine learning repository, 2017. URL http://archive.ics.uci.edu/ml. |
| Dataset Splits | Yes | We evaluate our method on the same open-access UCI datasets [4] as Wang et al. [25], using their same training, validation, and test partitions, and we compare against the baseline results they report. |
| Hardware Specification | Yes | using a single GPU (Tesla V100-SXM2-16GB for BT and BTE and Tesla V100-SXM2-32GB for the other methods). |
| Software Dependencies | No | No specific software versions (e.g., library or solver names with version numbers) were explicitly stated. |
| Experiment Setup | Yes | For the binary tree (BT) kernels, we use p = min(8, b150/dc + 1), and recall q = pd. We set λ = 1/n. We train the bit order and weights to minimize training NLL. For the binary tree ensemble (BTE), we use 20 kernels. |