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.
Budget-Constrained Multi-Armed Bandits With Multiple Plays
Authors: Datong Zhou, Claire Tomlin
AAAI 2018 | Venue PDF | LLM Run Details | Input Tokens: 16,594 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,230 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 | Firstly, we analyze this setting for the stochastic case... We derive an Upper Con๏ฌdence Bound (UCB) algorithm which achieves O(NK4 log B) regret. Secondly, for the adversarial case... we derive an upper bound on the regret of order O(NB log(N/K)) utilizing an extension of the well-known Exp3 algorithm. |
| Researcher Affiliation | Academia | Datong P. Zhou,1 Claire J. Tomlin2 Dept. of Mechanical Engineering, 2Dept. of Electrical Engineering and Computer Sciences University of California, Berkeley, CA 94720 EMAIL |
| Pseudocode | Yes | Algorithm 1 UCB-MB for Stochastic MAB |
| Open Source Code | No | The paper does not provide any explicit statement or link regarding the availability of open-source code for the methodology described. |
| Open Datasets | No | The paper is theoretical and does not use or refer to any specific publicly available datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not provide specific dataset split information (e.g., percentages, counts) for training, validation, or testing. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for running experiments. |
| Software Dependencies | No | The paper is theoretical and does not provide specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with specific hyperparameter values or training configurations. |