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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
One More Step Towards Reality: Cooperative Bandits with Imperfect Communication
Authors: Udari Madhushani, Abhimanyu Dubey, Naomi Leonard, Alex Pentland
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,466 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 12,597 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our proposed algorithms are straightforward to implement and obtain competitive empirical performance. |
| Researcher Affiliation | Academia | Udari Madhushani Princeton University EMAIL Abhimanyu Dubey Massachusetts Institute of Technology EMAIL Naomi Ehrich Leonard Princeton University EMAIL Alex Pentland Massachusetts Institute of Technology EMAIL |
| Pseudocode | Yes | Algorithm 1: RCL-RC: Cooperative Hybrid Arm Elimination |
| Open Source Code | No | The paper does not provide any explicit statements or links indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper describes generating data for experiments: "We consider the 10-armed bandit with rewards drawn from Gaussian distributions..." and "We draw delays from a bounded distribution..." and "We draw corruptions uniformly from the range..." but does not refer to using or providing access to a publicly available dataset. |
| Dataset Splits | No | The paper describes a multi-armed bandit simulation setup with agents and arms over time, not a dataset with explicit training, validation, and test splits. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments. |
| Software Dependencies | No | The paper mentions 'networkx' but does not specify a version number. It does not provide specific version numbers for any other software dependencies. |
| Experiment Setup | Yes | We consider the 10-armed bandit with rewards drawn from Gaussian distributions with ฯk = 1 for each arm, such that ยต1 = 1 and ยตk = 0.5 for k 6= 1, and the number of agents N = 50, where we repeat each experiment 100 times with G selected randomly from different families of random graphs. We set = 1.1 and ฮณ = max{3, d?(G)/2}. We draw delays from a bounded distribution with E[ ] = 10 and max = 50. We draw corruptions uniformly from the range [0, ] for each message, where is increased from 10 3 to 10 2. We use ฮณ = 2. |