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.
Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits
Authors: Yogev Bar-On, Yishay Mansour
NeurIPS 2019 | Venue PDF | LLM Run Details | Input Tokens: 15,272 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,202 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 | We derive regret minimization algorithms that guarantee for each agent v an individual expected regret of e O r 1 + K |N (v)| T , Our main contribution is an individual expected regret bound, which holds for each agent v, of order s 1 + K |N (v)|, We will now provide an overview for the analysis of our algorithms. We remind that all proofs are differed to the supplementary material. |
| Researcher Affiliation | Collaboration | Yogev Bar-On Tel Aviv University, Israel EMAIL Yishay Mansour Tel Aviv University, Israel and Google Research, Israel EMAIL |
| Pseudocode | Yes | Algorithm 1 Center-based cooperative MAB v is a center agent, Algorithm 2 Center-based cooperative MAB v is a non-center agent, Algorithm 3 Centers-to-Components, Algorithm 4 Compute-Centers-Informed, Algorithm 5 Compute-Centers-Uninformed. |
| Open Source Code | No | The paper does not contain any statement about making its source code publicly available, nor does it provide a link to a code repository. |
| Open Datasets | No | This is a theoretical paper that does not use or reference any datasets for training or evaluation. |
| Dataset Splits | No | This is a theoretical paper and does not describe any dataset splits (train, validation, test) as it does not involve empirical evaluation. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running experiments, as it is a theoretical work. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers, as it focuses on theoretical algorithms and their analysis. |
| Experiment Setup | No | This is a theoretical paper and does not provide details about an experimental setup, such as hyperparameters or training configurations, as it does not involve empirical experiments. |