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..
Decentralized Learning in Online Queuing Systems
Authors: Flore Sentenac, Etienne Boursier, Vianney Perchet
NeurIPS 2021 | Venue PDF | LLM Run Details | Input Tokens: 17,680 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 4,323 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 | Section 5 finally compares the behavior of ADEQUA with no-regret strategies on toy examples and empirically confirms the different known theoretical results. and Figures 2 and 3 compare on toy examples the stability of queues, when either each of them follows the no-regret strategy EXP3.P.1, or each queue follows ADEQUA. |
| Researcher Affiliation | Collaboration | Flore Sentenac CREST, ENSAE Paris, Palaiseau, France EMAIL, Etienne Boursier Centre Borelli, ENS Paris-Saclay, France EMAIL, Vianney Perchet CREST, ENSAE Paris, Palaiseau, France CRITEO AI Lab, Paris, France EMAIL |
| Pseudocode | Yes | Algorithm 1: ADEQUA and Algorithm 2: EXPLORE |
| Open Source Code | Yes | The code for the experiments is available at gitlab.com/f_sen/queuing_systems. |
| Open Datasets | No | The paper uses toy examples with specified parameters (e.g., λi = (N + 1)/N 2. Moreover µ1 = 1 and for all i 2, µi = (N 1)/N 2) rather than providing access information for a publicly available dataset. |
| Dataset Splits | No | The paper describes using toy examples for simulations but does not provide specific train/validation/test dataset split information. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details with version numbers required to replicate the experiment. |
| Experiment Setup | Yes | For practical considerations, we choose the exploration probability εt = (N + K)t 1/4 for ADEQUA, as the exploration is too slow with εt of order t 1/5. and The code for the experiments is available at gitlab.com/f_sen/queuing_systems. |