Communication-Efficient Collaborative Best Arm Identification
Authors: Nikolai Karpov, Qin Zhang
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We give both algorithmic and impossibility results, and conduct a set of experiments to demonstrate the effectiveness of our algorithms. |
| Researcher Affiliation | Academia | Indiana University Bloomington nkarpov@iu.edu, qzhangcs@indiana.edu |
| Pseudocode | Yes | The algorithm for top-m arm identification in the IID data setting is described in Algorithm 1. [...] Algorithm 2: LOCALELIM(r) [...] Algorithm 3: GLOBALELIM(r) |
| Open Source Code | No | The paper does not contain any explicit statement about releasing the source code for the methodology, nor does it provide a link to a code repository. |
| Open Datasets | Yes | We use a real-world dataset Movie Lens (Harper and Konstan 2016). |
| Dataset Splits | No | The paper mentions using the Movie Lens dataset but does not provide specific details on training, validation, or test splits (e.g., percentages, sample counts, or citations to predefined splits). |
| Hardware Specification | Yes | All experiments were conducted in Power Edge R740 server equipped 2 Intel Xeon Gold 6248R 3.0 GHz (24-core/48-thread per CPU) and 256GB RAM. |
| Software Dependencies | No | The paper states 'All algorithms are implemented using programming language Kotlin.' However, it does not provide specific version numbers for Kotlin or any other libraries/frameworks used. |
| Experiment Setup | No | The paper describes the algorithms and their performance, but it does not specify concrete hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations for the experiments. |