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-player Bandit Optimization
Authors: Ilai Bistritz, Nicholas Bambos
NeurIPS 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We demonstrate the numerical behavior of Algorithm 1 using a congestion game with N = 8 players and R = 4 resources. ... Figure 2: Congestion game with N = 8 players and R = 4 resources (a) Typical realization (b) Performance over 1000 Realizations |
| Researcher Affiliation | Academia | Ilai Bistritz, Nicholas Bambos Stanford University EMAIL |
| Pseudocode | Yes | Algorithm 1 Cooperative Multi-player Bandit Optimization |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of source code for the described methodology. |
| Open Datasets | No | The paper describes a 'Numerical Example' using a 'congestion game' simulation environment, not a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper describes a numerical simulation but does not provide specific details on dataset splits (e.g., train/validation/test percentages or counts). |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments, only describing a 'Numerical Example' simulation. |
| Software Dependencies | No | The paper does not mention any specific software dependencies with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | We used the step size sequence η (t) = 0.2 t0.9 and the sampling radius sequence δ (t) = 0.2 t0.1 . The memory was M = 6. Players broadcasted one reward value per turn, uniformly at random. |