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 [1].
Decentralized Learning in Online Queuing Systems
Authors: Flore Sentenac, Etienne Boursier, Vianney Perchet
NeurIPS 2021 | Venue PDF | LLM Run Details
| 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. |