Notice: 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. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Authors: Abhimanyu Dubey, Alex βSandyβ Pentland
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, on a series of both synthetic and real-world multi-agent network benchmarks, we demonstrate that our algorithm significantly outperforms existing benchmarks. |
| Researcher Affiliation | Academia | 1Media Lab and Institute for Data, Systems and Society, Massachusetts Institute of Technology. Correspondence to: Abhimanyu Dubey <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 COOP-KERNELUCB |
| Open Source Code | No | The paper does not provide a link to open-source code or explicitly state that the code is publicly available. |
| Open Datasets | Yes | For the synthetic networks, we subsample V nodes and their corresponding edges (for V = 200) from the ego-Facebook, musae-Twitch, and as-Skitter networks, in order to represent a diverse set of networks found in social networks, peer-to-peer distribution and autonomous systems. |
| Dataset Splits | No | The paper mentions generating contexts and running experiments, but does not specify explicit training, validation, or test dataset splits in terms of percentages or sample counts. It states: "Dv,t is a set of 8 randomly generated contexts for all v V, t T and dimensionality d = 10 for X and Z (for setup 2)." |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU/CPU models, memory, cloud instances). |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers. |
| Experiment Setup | Yes | For the kernel estimation task, we set Ο = 1, and we set Ξ» = 1. |