Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
Almost Cost-Free Communication in Federated Best Arm Identification
Authors: Srinivas Reddy Kota, P. N. Karthik, Vincent Y. F. Tan
AAAI 2023 | Venue PDF | LLM Run Details | Input Tokens: 17,063 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,882 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We numerically validate the efficacy of FEDELIM on two synthetic datasets and the Movie Lens dataset. |
| Researcher Affiliation | Academia | Srinivas Reddy Kota, P. N. Karthik, and Vincent Y. F. Tan National University of Singapore Emails: EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Federated Learning Successive Elimination Algorithm (FEDELIM) |
| Open Source Code | Yes | The code used for obtaining the results may be accessed at https://github.com/ pnkarthik/AAAI-2023-Code. |
| Open Datasets | Yes | We numerically validate the efficacy of FEDELIM on two synthetic datasets and the Movie Lens dataset. |
| Dataset Splits | No | The paper mentions using 'synthetic datasets' and 'Movie Lens dataset' but does not specify any explicit training, validation, or test splits (e.g., percentages, sample counts, or predefined splits) for these datasets. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory, or cloud instances) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies (e.g., library names with version numbers like Python 3.8, PyTorch 1.9) required to replicate the experiments. |
| Experiment Setup | No | The paper describes the FEDELIM algorithm and its confidence parameters, but it does not specify concrete hyperparameters (e.g., learning rate, batch size, number of epochs) or system-level training settings that are typically provided in an experimental setup. |