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..
Efficient Approximate Inference for Stationary Kernel on Frequency Domain
Authors: Yohan Jung, Kyungwoo Song, Jinkyoo Park
ICML 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we provide the experiments results validating the performances of the proposed model using the various data sets. |
| Researcher Affiliation | Academia | 1Department of Industrial & Systems Engineering, Korea Advanced Institute of Science and Technology, Daejeon , South Korea 2Department of Artificial Intelligence, University of Seoul, Seoul, South Korea. Correspondence to: Yohan Jung <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Approximate Inference for the spectral density parameters {wq, ยตq, ฯq}Q q=1 and noise parameter ฯฯต. |
| Open Source Code | Yes | We provide our implementation at https: //github.com/becre2021/ABInfer GSM and additional results in Appendix D. |
| Open Datasets | Yes | Passenger data set used in (Wilson & Adams, 2013). ... bike dataset (N=17379, D=17) in UCI benchmark set (Dua & Graff, 2017). |
| Dataset Splits | Yes | We equally divide five partitions of the dataset and randomly select the training, validation, and test set with a ratio of 8:1:1 for each partition. We pick the best kernel parameters with the lowest RMSE on validation set and used them for conducting predictions on test set. |
| Hardware Specification | Yes | We use Py Torch (1.7.0) (Paszke et al., 2019) and employ RTX2080TI-11GB and V100-16GB for GPU. |
| Software Dependencies | Yes | We use Py Torch (1.7.0) (Paszke et al., 2019) |
| Experiment Setup | Yes | For the baseline learning method (maximization of log marginal likelihood known as MLE-Type 2), SVSS, and SVSS-Ws, we use the Adam optimizer (Kingma & Ba, 2014) with the learning rate lr = .005. ... We run 1000 and 1200 iterations for training the Parkinsons and the Bike dataset, respectively. ... We run 1500 iterations for training. |