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.
Min-Max Submodular Ranking for Multiple Agents
Authors: Qingyun Chen, Sungjin Im, Benjamin Moseley, Chenyang Xu, Ruilong Zhang
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 16,689 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,880 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 | This section investigates the empirical performance of our algorithms. We seek to show that the theory is predictive of practice on real data. We give experimental results for the min-max optimal decision tree over multiple agents. |
| Researcher Affiliation | Academia | 1 Electrical Engineering and Computer Science, University of California at Merced 2 Tepper School of Business, Carnegie Mellon University 3 Software Engineering Institute, East China Normal University 4 College of Computer Science, Zhejiang University 5 Department of Computer Science, City University of Hong Kong |
| Pseudocode | Yes | Algorithm 1: Balanced Adaptive Greedy for SRMA |
| Open Source Code | No | The paper does not provide explicit links to source code for the methodology or state that the code is publicly available. |
| Open Datasets | Yes | In the experiments, three public data sets are considered: MFCC data set3, PPPTS data set4, and CTG data set5. 3https://archive.ics.uci.edu/ml/datasets/Anuran+Calls+\% 28MFCCs\%29 4https://archive.ics.uci.edu/ml/datasets/Physicochemical+ Properties+of+Protein+Tertiary+Structure# 5https://archive.ics.uci.edu/ml/datasets/Cardiotocography |
| Dataset Splits | No | The paper mentions data preparation and uses public datasets but does not specify any training, validation, or test splits (e.g., percentages or sample counts). |
| Hardware Specification | Yes | We conduct the experiments on a machine running Ubuntu 18.04 with an i7-7800X CPU and 48 GB memory. |
| Software Dependencies | No | The paper mentions the operating system "Ubuntu 18.04" but does not specify any other software dependencies like programming languages, libraries, or frameworks with version numbers. |
| Experiment Setup | Yes | In the experiments, we test the performance of algorithm BAG with decreasing ratios in [0, 0.05, 0.1, . . . , 0.95, 1] and pick the best decreasing ratio. |