Kernel Methods for Cooperative Multi-Agent Contextual Bandits
Authors: Abhimanyu Dubey, Alex ‘Sandy’ Pentland
ICML 2020 | Conference PDF | Archive PDF | Plain Text | 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 <dubeya@mit.edu>. |
| 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 σ = 1, and we set λ = 1. |