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.
Thompson Sampling for Bandit Learning in Matching Markets
Authors: Fang Kong, Junming Yin, Shuai Li
IJCAI 2022 | Venue PDF | LLM Run Details | Input Tokens: 16,035 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,788 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments demonstrate the practical advantages of the TS-type algorithm over the ETC and UCB-type baselines. In this section, we compare the performances of our CA-TS with other related baselines in different environments |
| Researcher Affiliation | Academia | Fang Kong1 , Junming Yin2 and Shuai Li1 1John Hopcroft Center for Computer Science, Shanghai Jiao Tong University 2Tepper School of Business, Carnegie Mellon University |
| Pseudocode | Yes | Algorithm 1 Con๏ฌict-Avoiding TS (CA-TS) |
| Open Source Code | Yes | The code is available at https://github.com/fangkongx/TSforMatchingMarkets. |
| Open Datasets | No | The paper uses synthetically generated data: 'we construct a market of N = 5 players and K = 5 arms with global preferences...' and 'The feedback Xi,j(t) for player pi on arm aj at time t is drawn independently from Bernoulli(ยตi,j).' No concrete access information for a publicly available or open dataset is provided. |
| Dataset Splits | No | The paper describes an online learning setting where data is generated iteratively. It states, 'In each market, we run all algorithms for T = 100k rounds and all results are averaged over 50 independent runs.' There is no mention of specific training/validation/test dataset splits, as this is not applicable to the problem setup with iteratively collected rewards. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as CPU/GPU models, memory, or type of computing environment used for running the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, frameworks, or solvers). |
| Experiment Setup | Yes | The hyper-parameters of all baselines are set as their original paper with details in the Appendix. We run all algorithms for T = 100k rounds and all results are averaged over 50 independent runs. we set the preference value towards the least favorite arm as 0.1 for all players and test four different choices of reward gap between any two consecutively ranked arms {0.2, 0.15, 0.1, 0.05}. |