Notice: 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. Full accuracy metrics and methodology are described in [1].
Anytime Capacity Expansion in Medical Residency Match by Monte Carlo Tree Search
Authors: Kenshi Abe, Junpei Komiyama, Atsushi Iwasaki
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | This section empirically evaluates our algorithm via synthetic and real datasets. We compare our algorithms with the ones by [Bobbio et al., 2021] |
| Researcher Affiliation | Collaboration | 1Cyber Agent, Inc. 2New York University 3University of Electro-Communications |
| Pseudocode | No | The paper states 'For a more algorithmic description, see the full version.' but does not include pseudocode or an algorithm block in the provided text. |
| Open Source Code | Yes | An implementation of our method is available at https://github. com/Cyber Agent AILab/mcts-capacity-expansion. |
| Open Datasets | Yes | Second, we generate the dataset, JRMP, based on Japan Residency Matching Program 2007 [Kamada and Kojima, 2015], which matches medical hospital students (residents) with residency training programs. |
| Dataset Splits | No | The paper does not specify exact training, validation, or test dataset splits. It describes data generation parameters and the size of the JRMP dataset but not how it's partitioned for experimentation. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU or GPU models, or cloud computing resources. |
| Software Dependencies | No | The paper mentions 'Python 3' and 'Gurobi' but does not specify version numbers for Gurobi or other key software libraries used in the experiments. |
| Experiment Setup | Yes | For our UCT method, we set N = B 103 in synthetic data experiments, N = B 102 in real data experiments. The value of Cp, which determines the tradeoff between exploration and exploitation, is set to be 0.002. |