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].
Improved Bandits in Many-to-One Matching Markets with Incentive Compatibility
Authors: Fang Kong, Shuai Li
AAAI 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We ๏ฌrst 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 ๏ฌrst 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. |