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].
Maximizing and Satisficing in Multi-armed Bandits with Graph Information
Authors: Parth Thaker, Mohit Malu, Nikhil Rao, Gautam Dasarathy
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We show additional experiments with different graph parameters for Stochastic block model and Barabási-Albert graphs and different cluster sizes as well as real data in Appendix K. The full code used for conducting experiments can be found at the following Github repository. For all our experiments, we use Intel R Core TM i7-10875H CPU @ 2.30GHz 16 with 32 GB memory. We set δ = 1e 3, ρ = 2.0, σ = 2.0. We evaluate GRUB with different sampling strategies from section J and compare its performance to standard UCB algorithm on both synthetic and real datasets. |
| Researcher Affiliation | Collaboration | Parth K. Thaker Arizona State University EMAIL Mohit Malu Arizona State University EMAIL Nikhil Rao Microsoft EMAIL Gautam Dasarathy Arizona State University EMAIL |
| Pseudocode | Yes | The pseudocode for GRUB can be found in Appendix E. The pseudocode for the ζ-GRUB can be found in Appendix G. |
| Open Source Code | Yes | The full code used for conducting experiments can be found at the following Github repository. |
| Open Datasets | No | The paper states it uses 'Synthetic Data' generated from models like 'Stochastic Block Model' and 'Barabási Albert graph', and 'real data in Appendix K'. While the ethics checklist mentions 'Data is public', the main text does not provide specific citations, links, or names of well-known public datasets for which access information is directly provided. |
| Dataset Splits | No | The paper does not provide specific dataset split information (e.g., percentages or counts for training, validation, or test sets). It mentions 'training details' in the checklist and parameters like δ, ρ, σ, but not how the data was partitioned into splits for reproduction. |
| Hardware Specification | Yes | For all our experiments, we use Intel R Core TM i7-10875H CPU @ 2.30GHz 16 with 32 GB memory. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library or solver names with version numbers) needed to replicate the experiment. |
| Experiment Setup | Yes | For all our experiments, we use Intel R Core TM i7-10875H CPU @ 2.30GHz 16 with 32 GB memory. We set δ = 1e 3, ρ = 2.0, σ = 2.0. |