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..
Log-Linear-Time Gaussian Processes Using Binary Tree Kernels
Authors: Michael K. Cohen, Samuel Daulton, Michael A Osborne
NeurIPS 2022 | Venue PDF | 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 EMAIL Samuel Daulton University of Oxford, Meta EMAIL Michael A. Osborne University of Oxford EMAIL |
| 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. |