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].
Sparse Variational Inference: Bayesian Coresets from Scratch
Authors: Trevor Campbell, Boyan Beronov
NeurIPS 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section, we compare the quality of coresets constructed via the proposed Sparse VI greedy coreset construction method, uniform random subsampling, and Hilbert coreset construction (GIGA [32]). |
| Researcher Affiliation | Academia | Trevor Campbell Department of Statistics University of British Columbia Vancouver, BC V6T 1Z4 EMAIL Boyan Beronov Department of Computer Science University of British Columbia Vancouver, BC V6T 1Z4 EMAIL |
| Pseudocode | Yes | Algorithm 1 Greedy sparse stochastic variational inference |
| Open Source Code | Yes | code is available at www.github.com/trevorcampbell/bayesian-coresets. |
| Open Datasets | Yes | This dataset was constructed by merging housing prices from the UK land registry data https://www.gov.uk/government/statistical-data-sets/price-paid-data-downloads with latitude & longitude coordinates from the Geonames postal code data http://download.geonames.org/export/zip/. |
| Dataset Splits | No | The paper describes the datasets used and the overall experiment settings (e.g., number of samples, iterations) but does not provide specific train, validation, or test dataset split percentages or counts, nor does it reference standard dataset splits for reproduction. |
| Hardware Specification | Yes | Experiments were performed on a machine with an Intel i7 8700K processor and 32GB memory |
| Software Dependencies | No | The paper describes the algorithms and computational environment (e.g., processor, memory) but does not specify software dependencies with version numbers (e.g., specific Python, PyTorch, or library versions). |
| Experiment Setup | Yes | We used a learning rate of Îłt = t^-1, T = 100 weight update optimization iterations, and M = 200 greedy iterations, although note that this is an upper bound on the size of the coreset as the same data point may be selected multiple times. |