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..
Beyond $log^2(T)$ regret for decentralized bandits in matching markets
Authors: Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman
ICML 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We further demonstrate superiority of our algorithm over existing works through simulations. ... Extensive simulations show, despite the restrictive feedback, phased ETC outperforms CA-UCB in general instances, while UCB-D4 does so under uniqueness consistency. |
| Researcher Affiliation | Industry | 1Google, Mountain View, CA, USA 2Facebook, Menlo Park, CA, USA 3Amazon, Palo Alto, CA, USA. |
| Pseudocode | Yes | Algorithm 1 Phased ETC Algorithm. ... Algorithm 2 UCB-D4 algorithm (for an agent j) |
| Open Source Code | No | The paper does not include any explicit statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | No | The paper states: 'We generate random instances to compare the performance...'. This indicates the use of synthetically generated data rather than a publicly available dataset with concrete access information. |
| Dataset Splits | No | The paper mentions 'We simulate all the algorithms on the same sample paths, for a total 50 sample paths' but does not specify any training, validation, or test dataset splits. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU models, CPU types, or memory used for running the experiments or simulations. |
| Software Dependencies | No | The paper mentions baselines (UCB-C, CA-UCB) but does not specify any software names with version numbers or other ancillary software dependencies used in the experiments. |
| Experiment Setup | Yes | For UCB-D4, we use β = 1/2K and γ = 2. |