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.
Improved Bandits in Many-to-One Matching Markets with Incentive Compatibility
Authors: Fang Kong, Shuai Li
AAAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 18,425 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,117 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We ο¬rst extend an existing algorithm for the oneto-one setting to this more general setting and show it achieves a near-optimal bound for player-optimal regret. ... We ο¬rst propose the adaptively explore-then-deferred-acceptance (AETDA) algorithm for responsiveness setting and derive an upper bound for player-optimal stable regret while demonstrating its guarantee of incentive compatibility. ... We devise an online DA (ODA) algorithm and establish an upper bound for the player-pessimal stable regret for this setting. |
| Researcher Affiliation | Academia | John Hopcroft Center for Computer Science, Shanghai Jiao Tong University EMAIL |
| Pseudocode | Yes | Algorithm 1: centralized adaptively explore-then-deferred-acceptance (AETDA, from the view of the central platform) ... Algorithm 2: online deferred acceptance (from view of pi) |
| Open Source Code | No | The paper does not provide any statement about releasing open-source code or a link to a code repository. |
| Open Datasets | No | The paper is theoretical and does not refer to any datasets used for training. |
| Dataset Splits | No | The paper is theoretical and does not describe any dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not mention specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe any experimental setup details such as hyperparameters or training settings. |