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..
Computation-Aware Gaussian Processes: Model Selection And Linear-Time Inference
Authors: Jonathan Wenger, Kaiwen Wu, Philipp Hennig, Jacob Gardner, Geoff Pleiss, John P. Cunningham
NeurIPS 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our experiments show that model selection for computation-aware GPs trained on 1.8 million data points can be done within a few hours on a single GPU. |
| Researcher Affiliation | Academia | Jonathan Wenger1 Kaiwen Wu2 Philipp Hennig3 Jacob R. Gardner2 Geoff Pleiss4 John P. Cunningham1 1 Columbia University 2 University of Pennsylvania 3 University of T ubingen, T ubingen AI Center 4 University of British Columbia, Vector Institute |
| Pseudocode | Yes | We give algorithms both for iteratively constructed dense actions (Algorithm S1), as used in Ca GPCG, and for sparse batch actions (Algorithm S2), as used for Ca GP-Opt, in the supplementary material. |
| Open Source Code | Yes | We provide an open-source implementation of Ca GP-Opt as part of GPy Torch. To install the package via pip, execute the following in the command line: pip install git+https://github.com/cornellius-gp/linear_operator.git@sparsity pip install git+https://github.com/cornellius-gp/gpytorch.git@computation-aware-gps-v2 pip install pykeops |
| Open Datasets | Yes | We benchmark the generalization of computation-aware GPs with two different action choices, Ca GP-Opt (ours) and Ca GP-CG [19], using our proposed training objective in Equation (10) on a range of UCI datasets for regression [53]. |
| Dataset Splits | Yes | All datasets were randomly partitioned into train and test sets using a (0.9, 0.1) split for five random seeds. |
| Hardware Specification | Yes | All experiments were run on an NVIDIA Tesla V100-PCIE-32GB GPU, except for Power , where we used an NVIDIA A100 80GB PCIe GPU to have sufficient memory for Ca GP-Opt with i = 512. |
| Software Dependencies | No | We used the existing implementations of SGPR, SVGP and CGGP in GPy Torch [7] and also implemented Ca GP in this framework (see Section S4.2 for our open-source implementation). ... pip install pykeops |
| Experiment Setup | Yes | We used a zero-mean GP prior and a Mat ern(3/2) kernel with an outputscale o2 and one lengthscale per input dimension l2 j, as well as a scalar observation noise for the likelihood σ2, s.t. θ = (o, l1, . . . , ld, σ) Rd+2. ... For SGPR and SVGP we used m = 1024 inducing points and for CGGP, Ca GP-CG and Ca GP-Opt we chose i = 512. We optimized the hyperparameters θ either with Adam [54] for a maximum of 1000 epochs in float32 or with LBFGS [55] for 100 epochs in float64, depending on the method and problem scale. |