Min-Max Submodular Ranking for Multiple Agents
Authors: Qingyun Chen, Sungjin Im, Benjamin Moseley, Chenyang Xu, Ruilong Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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. |