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.
Provable Submodular Minimization using Wolfe's Algorithm
Authors: Deeparnab Chakrabarty, Prateek Jain, Pravesh Kothari
NeurIPS 2014 | Venue PDF | LLM Run Details | Input Tokens: 14,806 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,841 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 | Our analysis suggests that the Fujishige-Wolfe s algorithm is dependent on F and has worse dependence on n than the Iwata-Orlin [11] algorithm. To verify this, we conducted empirical study on several standard SFM problems. |
| Researcher Affiliation | Collaboration | Microsoft Research, 9 Lavelle Road, Bangalore 560001. University of Texas at Austin (Part of the work done while interning at Microsoft Research) |
| Pseudocode | Yes | Algorithm 1 Wolfe s Algorithm |
| Open Source Code | No | The paper does not contain any statement or link regarding the availability of open-source code for the described methodology. |
| Open Datasets | Yes | In Figure 1 (a), we run both on Erdos-Renyi graphs with p = 0.8 and randomly chosen s, t nodes. In Figure 1 (b), we run both on the Iwata group functions [16] with 3 groups. |
| Dataset Splits | No | The paper discusses empirical studies but does not provide specific details on training, validation, or test dataset splits. |
| Hardware Specification | No | The paper describes empirical studies but does not provide any specific hardware specifications such as CPU or GPU models used for the experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers for replication. |
| Experiment Setup | Yes | In Figure 1 (a), we run both on Erdos-Renyi graphs with p = 0.8 and randomly chosen s, t nodes. In Figure 1 (b), we run both on the Iwata group functions [16] with 3 groups. Perhaps more interestingly, in Figure 1 (c), we ran the Fujishige-Wolfe algorithm on the simple path graph where s, t were the end points, and changed the capacities on the edges of the graph which changed the parameter F. |