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.
Parallel Online Clustering of Bandits via Hedonic Game
Authors: Xiaotong Cheng, Cheng Pan, Setareh Maghsudi
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 24,689 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,046 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 | We evaluate the performance of our algorithm using synthetic and real-world datasets. Besides, we compare the results to some state-of-the-art bandit and clustering of bandits algorithms. |
| Researcher Affiliation | Academia | 1Department of Computer Science, University of T ubingen, T ubingen, Germany. |
| Pseudocode | Yes | Algorithm 1 CLUB-HG; Algorithm 2 Hedonic Clustering Game |
| Open Source Code | No | The paper does not provide any explicit statement about releasing source code or a link to a code repository. |
| Open Datasets | Yes | Netflix Dataset. Netflix Movie Rating Dataset from Netflix s Netflix Prize competition on https://www.kaggle.com/datasets/ rishitjavia/netflix-movie-rating-dataset? resource=download; Movie Lens Dataset. Movie Lens 25M Movie Ratings Dataset on https:// grouplens.org/datasets/movielens/ |
| Dataset Splits | No | The paper describes data extraction and processing (e.g., for Netflix, 'We extract 103 movies with the most ratings and n = 200 users...'), but it does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | No | The paper does not provide specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not mention specific software dependencies with version numbers (e.g., Python, PyTorch, TensorFlow versions, or specific library versions). |
| Experiment Setup | Yes | Input: Exploration parameter: αj(t), hedonic clustering accuracy parameter βt.; Initialization bi,0 = 0 Rd and M i,0 = I Rd d, i V ; Clusters ˆV1,1 = V , number of clusters m1 = 1; We set L = 10, d = 5 and T = 1000. ... We set σ = 0.1. All regret plots are based on the average results of 20 independent runs. |