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.
Stochastic Rising Bandits
Authors: Alberto Maria Metelli, Francesco Trovò, Matteo Pirola, Marcello Restelli
ICML 2022 | Venue PDF | LLM Run Details | Input Tokens: 36,999 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,469 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 empirically compare our algorithms with state-of-the-art methods for non-stationary MABs over several synthetically generated tasks and an online model selection problem for a realworld dataset. |
| Researcher Affiliation | Academia | 1Dipartimento di Elettronica, Informazione e Bioingegneria, Politecnico di Milano, Milan, Italy. |
| Pseudocode | Yes | Algorithm 1 R-l-UCB (l Ptless,edu ) |
| Open Source Code | Yes | The code to reproduce the experiments is available at https://github.com/albertometelli/ stochastic-rising-bandits. |
| Open Datasets | Yes | We employ the IMDB dataset, made of 50,000 reviews of movies (scores from 0 to 10). We preprocessed the data as done by Maas et al. (2011) to obtain a binary classification problem. |
| Dataset Splits | No | The paper describes the use of datasets for evaluation, but it does not specify explicit training, validation, and test splits (e.g., percentages or counts) or reference a standard splitting methodology. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running the experiments (e.g., GPU models, CPU specifications, or memory). |
| Software Dependencies | No | The paper mentions various algorithms and packages (e.g., 'KL-UCB', 'Ser4', 'SW-UCB', 'SW-TS') but does not specify the version numbers for any software dependencies required to reproduce the experiments. |
| Experiment Setup | Yes | The parameters for all the baseline algorithms have been set as recommended in the corresponding papers (see also Appendix E). For our algorithms, the window is set as hi,t tϵNi,t 1u (as prescribed by Theorems 4.4 and 5.3). ...setting ϵ 1{4. ...R-ed-UCB, with ϵ 1{32. |