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.
Doubly Adversarial Federated Bandits
Authors: Jialin Yi, Milan Vojnovic
ICML 2023 | Venue PDF | LLM Run Details | Input Tokens: 20,162 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 5,785 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 provide numerical evaluations of our algorithm to validate our theoretical results and demonstrate its effectiveness on synthetic and real-world datasets. |
| Researcher Affiliation | Academia | 1Department of Statistics, London School of Economics and Political Science, London, United Kingdom. |
| Pseudocode | Yes | Algorithm 1 FEDEXP3 |
| Open Source Code | Yes | The code for producing our experimental results is available online in an anonymous Github repository: https://github.com/jialinyi94/doubly-stochasticfederataed-bandit. |
| Open Datasets | Yes | We evaluate the two algorithms using the Movie Lens Latest-full dataset which contains 58,000 movies, classified into 20 genres, with 27,000,000 ratings (rating scores in {0.5, 1, . . . , 5}) from 280,000 users. |
| Dataset Splits | No | The paper describes the construction of synthetic datasets and the use of the Movie Lens Latest-full dataset, but does not specify explicit training, validation, or test dataset splits (e.g., percentages or sample counts for each split). |
| Hardware Specification | Yes | All the experiments are run on a desktop with AMD Ryzen 5 2600 Six-Core Processor and 16GB memory. |
| Software Dependencies | No | The paper mentions 'Python', 'Numpy package', and 'Network X package' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | We set T = 3, 000 with N = 36 and K = 20. At each time step t, a subset Ut of N/2 agents are selected from V with replacement. For all activated agents Ut, the loss for arm i is sampled independently from Bernoulli distribution with mean µi = (i − 1)/(K − 1). |