Learning for Edge-Weighted Online Bipartite Matching with Robustness Guarantees
Authors: Pengfei Li, Jianyi Yang, Shaolei Ren
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we run empirical experiments to demonstrate the advantages of LOMAR compared to existing baselines. |
| Researcher Affiliation | Academia | Pengfei Li 1 Jianyi Yang 1 Shaolei Ren 1 1University of California, Riverside, CA 92521, United States. |
| Pseudocode | Yes | Algorithm 1 Inference of Robust Learning-based Online Bipartite Matching (LOMAR) |
| Open Source Code | No | Our implementation of all the considered algorithms, including LOMAR, is based on the source codes provided by (Alomrani et al., 2022) |
| Open Datasets | Yes | We choose the Movie Lens dataset (Harper & Konstan, 2015), which provides a total of 3952 movies, 6040 users and 100209 ratings. |
| Dataset Splits | No | The number of graph instances in the training and testing datasets are 20000 and 1000, respectively. |
| Hardware Specification | Yes | training the RL model in LOMAR usually takes less than 8 hours on a shared research cluster with one NVIDIA K80 GPU |
| Software Dependencies | No | The paper mentions 'Gurobi optimizer' but does not specify its version number or any other software dependencies with version numbers. |
| Experiment Setup | Yes | For applicable algorithms (i.e., DRL, DRL-OS, and LOMAR), we train the RL model for 300 epochs in the training dataset with a batch size of 100. In LOMAR, the parameter B = 0 is used to follow the strict deļ¬nition of competitive ratio. ... Our RL architecture has 3 fully connected layers, each with 100 hidden nodes. |