Beyond $log^2(T)$ regret for decentralized bandits in matching markets
Authors: Soumya Basu, Karthik Abinav Sankararaman, Abishek Sankararaman
ICML 2021 | Conference PDF | Archive PDF | Plain Text | 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. |