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 Stochastic Multi-Armed Bandits
Authors: Idan Barnea, Tal Lancewicki, Yishay Mansour
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 42,173 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,856 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We prove that Coop-SE (Algorithm 3) achieves a near-optimal individual regret bound of O(R/m + A2 + A p log(T)), which is independent of the graph s diameter. |
| Researcher Affiliation | Collaboration | Idan Barnea Blavatnik School of Computer Science and AI Tel Aviv University, Israel EMAIL; Yishay Mansour Blavatnik School of Computer Science and AI Tel Aviv University, Israel Google Research, Tel Aviv, Israel |
| Pseudocode | Yes | Algorithm 1 Stochastic MAB on Graph. Protocol for agent v |
| Open Source Code | No | The paper does not include experiments requiring code. |
| Open Datasets | No | The paper does not include experiments. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |