Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Parallel Double Greedy Submodular Maximization
Authors: Xinghao Pan, Stefanie Jegelka, Joseph E Gonzalez, Joseph K. Bradley, Michael I Jordan
NeurIPS 2014 | Venue PDF | LLM Run Details | Input Tokens: 15,842 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,153 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We implement and evaluate both algorithms on multi-core hardware and billion edge graphs, demonstrating both the scalability and tradeoffs of each approach. |
| Researcher Affiliation | Academia | 1Department of Electrical Engineering and Computer Science, and 2Department of Statistics University of California, Berkeley, Berkeley, CA USA 94720 EMAIL |
| Pseudocode | Yes | Algorithm 3: Ser-2g: serial double greedy, Algorithm 4: CF-2g: coord-free double greedy, Algorithm 5: CC-2g: concurrency control, Algorithm 6: CC-2g get Guarantee(e), Algorithm 7: CC-2g propose, Algorithm 8: CC-2g: commit(e, i, ue, result) |
| Open Source Code | No | The paper does not provide any statement or link regarding the public release of its source code. |
| Open Datasets | Yes | Our parallel algorithms were tested on the max graph cut and set cover problems with two synthetic graphs and three real datasets (Table 1). Friendster [21] Arabic-2005 [22, 23, 24] UK-2005 [22, 23, 24] IT-2004 [22, 23, 24]. |
| Dataset Splits | No | The paper applies its algorithms to entire graph datasets and does not mention specific train/validation/test splits for the input data in the traditional machine learning sense. |
| Hardware Specification | Yes | Experiments were conducted on Amazon EC2 using one cc2.8xlarge machine, up to 16 threads, for 10 repetitions. |
| Software Dependencies | No | We implemented the parallel and serial double greedy algorithms in Java / Scala. |
| Experiment Setup | Yes | Experiments were conducted on Amazon EC2 using one cc2.8xlarge machine, up to 16 threads, for 10 repetitions. ... We randomly permuted the ordering of vertices. |