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 [1].
Efficient Gaussian Process Classification Using Pólya-Gamma Data Augmentation
Authors: Florian Wenzel, Théo Galy-Fajou, Christan Donner, Marius Kloft, Manfred Opper5417-5424
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the algorithm on real-world datasets containing up to 11 million data points and demonstrate that it is up to two orders of magnitude faster than the state-of-the-art while being competitive in terms of prediction performance. |
| Researcher Affiliation | Academia | 1TU Kaiserslautern, Germany, 2TU Berlin, Germany, 3University of Southern California, USA |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is available via Github1. 1https://github.com/theogf/Augmented Gaussian Processes.jl |
| Open Datasets | Yes | We experiment on 12 datasets from the Open ML website and the UCI repository ranging from 768 to 11 million data points. |
| Dataset Splits | Yes | For each dataset we perform a 10-fold cross-validation and for datasets with more than 1 million points, we limit the test set to 100,000 points. |
| Hardware Specification | No | The paper only states that 'All algorithms are run on a single CPU.' without providing specific CPU models or other hardware details. |
| Software Dependencies | Yes | We use GPflow version 1.2.0. |
| Experiment Setup | Yes | The kernel hyperparameters are initialized to the same values and optimized using Adam (Kingma and Ba 2014), while inducing points location are initialized via k-means++ (Arthur and Vassilvitskii 2007) and kept fixed during training. For all datasets, we use 100 inducing points and a mini-batch size of 100 points. For X-GPC we find that the following simple convergence criterion on the global parameters leads to good results: a sliding window average being smaller than a threshold of 10 4 . |