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..
The FAST Algorithm for Submodular Maximization
Authors: Adam Breuer, Eric Balkanski, Yaron Singer
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show that FAST is orders of magnitude faster than any algorithm for submodular maximization we are aware of, including hyper-optimized parallel versions of state-of-the-art serial algorithms, by running experiments on large data sets. |
| Researcher Affiliation | Academia | 1Harvard University, Cambridge, MA. Correspondence to: Adam Breuer <EMAIL>, Eric Balkanski <EMAIL>, Yaron Singer <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 FAST-FULL: the full algorithm |
| Open Source Code | Yes | Code is available from www.adambreuer.com/code. |
| Open Datasets | Yes | To compare the algorithms runtimes on real data, we optimize Sensor Placement on California roadway traffic data; Movie Recommendation on Movie Lens data; Revenue Maximization on You Tube Network data; and Influence Maximization on Facebook Network data. See Appendix C.4.3 for additional details. |
| Dataset Splits | No | No specific details on dataset splits (e.g., train/validation/test percentages or counts) are provided in the main text. |
| Hardware Specification | Yes | We then use 95 Intel Skylake-SP 3.1 GHz processors on AWS to compare the algorithms runtime over a variety of objectives defined on 8 real and synthetic datasets. |
| Software Dependencies | No | The paper mentions 'optimized parallel MPI implementations' but does not provide specific version numbers for MPI or any other software dependencies. |
| Experiment Setup | Yes | To obtain these values, we set all algorithms to guarantee a 1 1/e 0.1 approximation with probability 0.95 except LTLG, which has this guarantee in expectation, and we set k = 200. |