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..
Bayesian Batch Active Learning as Sparse Subset Approximation
Authors: Robert Pinsler, Jonathan Gordon, Eric Nalisnick, José Miguel Hernández-Lobato
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the benefits of our approach on several large-scale regression and classification tasks. and 7 Experiments and results |
| Researcher Affiliation | Academia | Robert Pinsler Department of Engineering University of Cambridge EMAIL Jonathan Gordon Department of Engineering University of Cambridge EMAIL Eric Nalisnick Department of Engineering University of Cambridge EMAIL José Miguel Hernández-Lobato Department of Engineering University of Cambridge EMAIL |
| Pseudocode | Yes | The complete AL procedure, Active Bayesian Core Sets with Frank-Wolfe optimization (ACS-FW), is outlined in Appendix A (see Algorithm A.1). |
| Open Source Code | Yes | Source code is available at https://github.com/rpinsler/active-bayesian-coresets. |
| Open Datasets | Yes | We evaluate the performance of ACS-FW on several UCI regression datasets. and on the classification datasets cifar10, SVHN and Fashion MNIST. These are widely recognized public datasets. |
| Dataset Splits | No | The paper describes 'randomized 80/20% train-test splits' for regression tasks and 'holdout test set' with the remainder for training for classification tasks, but does not explicitly mention a separate validation split or provide details on how a validation set was used for hyperparameter tuning or early stopping. |
| Hardware Specification | No | The paper does not provide specific hardware details such as exact GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper mentions 'Adam [36]' as an optimizer and 'Py Torch' but does not provide specific version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | The model is re-trained for 1000 epochs after every AL iteration using Adam [36]. and trained from scratch at every AL iteration for 250 epochs using Adam [36]. and Further details, including architectures and learning rates, are in Appendix C. |