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..
Cooperative Multi-Agent Bandits with Heavy Tails
Authors: Abhimanyu Dubey, Alex ‘Sandy’ Pentland
ICML 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments using α-stable densities (L evy, 1925), that admit finite moments only of order < α 2, and we consider α-stable densities where α 1. The α-stable family includes several widely used distributions, such as Gaussian (α = 2, only lighttailed density), L evy (α = 0.5) and Cauchy (α=1). |
| 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 DECENTRALIZED MP-UCB; Algorithm 2 CENTRALIZED MP-UCB; Algorithm 3 ONLINE TRIMMED MEAN ESTIMATOR |
| Open Source Code | No | The paper does not contain any explicit statements about releasing source code or providing links to code repositories for the described methodology. |
| Open Datasets | Yes | We select the p2p-Gnutella04 (Figure 1C) and ego-Facebook (Figure 1D) network structures from the SNAP repository (Leskovec & Sosiˇc, 2016) to experiment with in the real-world setting. |
| Dataset Splits | No | The paper describes generating random graphs ('Erdos-Renyi (ER) (p = 0.7) and Barabasi-Albert (BA) (m = 5) random graph families') and sampling subgraphs from real-world networks ('subgraphs of 500 nodes'). However, it does not specify any explicit train/validation/test dataset splits or cross-validation setups. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory, or cloud instance types) used for running the experiments. |
| Software Dependencies | No | The paper mentions using 'the approximate algorithm presented in (Lucas, 2014) that uses the QUBO (Glover & Kochenberger, 2018) solver' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | We set K = 5, α = 1.9 for the standard α-stable density, and sample arm means randomly from the interval [0, 1] for each arm every experiment. We then construct random graphs on 200 agents from the Erdos-Renyi (ER) (p = 0.7) and Barabasi-Albert (BA) (m = 5) random graph families, and compare all three of our algorithms (using the trimmed mean estimator, with γ = diam(G)/2) with the CONSENSUS-UCB and single-agent ROBUST-UCB(Bubeck et al., 2013) algorithms. |