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 Budgeted Multi-Armed Bandits
Authors: Yingce Xia, Haifang Li, Tao Qin, Nenghai Yu, Tie-Yan Liu
IJCAI 2015 | Venue PDF | LLM Run Details | Input Tokens: 17,105 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,856 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 conduct a set of numerical simulations with different rewards/costs distributions and different number of arms. The simulation results demonstrate the effectiveness of the proposed algorithm. |
| Researcher Affiliation | Collaboration | 1University of Science and Technology of China, Hefei, China 2 University of Chinese Academy of Sciences, Beijing, China 3Microsoft Research, Beijing, China |
| Pseudocode | Yes | Algorithm 1 Budgeted Thompson Sampling (BTS) |
| Open Source Code | No | The paper does not provide any explicit statement or link for open-source code. |
| Open Datasets | No | The paper uses simulated data based on Bernoulli and Multinomial distributions and does not refer to any public, accessible datasets with specific access information. |
| Dataset Splits | No | The paper does not provide specific training/validation/test dataset splits. It describes simulations with randomly chosen parameters and running experiments for 500 times. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models) used for running the experiments. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers. |
| Experiment Setup | Yes | For comparison purpose, we implement four baseline algorithms: (1) the ϵ-first algorithm [Tran-Thanh et al., 2010] with ϵ = 0.1; (2) a variant of the PD-Bw K algorithm... ϕ(x, N) = p νx /N and ν = 0.25 log(BK); (3) the UCB-BV1 algorithm [Ding et al., 2013]; (4) a variant of the KUBE algorithm... We simulate bandits with two different distributions: one is the Bernoulli distribution (simple), and the other is the multinomial distribution (complex). Their parameters are randomly chosen. For each distribution, we simulate a 10-armed case and a 100-armed case. We then independently run the experiments for 500 times... B = {100, 200, 500, 1K, 2K, 5K, 10K, 15K, 20K, ..., 50K}. |