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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Graph-Triggered Rising Bandits
Authors: Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli
ICML 2024 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this appendix, we provide an experimental campaign to validate the proposed algorithmic solutions from an empirical perspective. We start with the deterministic setting: in Appendix D.1 we evaluate DR-BG-UB in 15 GTRB instances, varying both the functions and the adjacency matrices; in Appendix D.2 we evaluate DR-G-UB in 3 GTRB instances, but varying the sub-matrix used in the Algorithm 2 routine. Finally, we evaluate R-UCB in 10 stochastic GTRB instances, varying both the functions and the adjacency matrices, and comparing its performances to a baseline from the literature, Sliding Window UCB (Garivier & Moulines, 2011). |
| Researcher Affiliation | Academia | 1Politecnico di Milano, Milan, Italy. Correspondence to: G. Genalti <EMAIL>. |
| Pseudocode | Yes | Algorithm 1: DR-BG-UB. Algorithm 2: DR-G-UB. Algorithm 3: R-UCB. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the methodology described, such as a specific repository link or an explicit code release statement. |
| Open Datasets | No | The paper conducts experiments on synthetic environments using 'sets of functions' (Figure 2, Figure 4) rather than referencing established public datasets with access information or formal citations. |
| Dataset Splits | No | The paper describes synthetic experimental settings but does not provide specific dataset split information (percentages, sample counts, or citations to predefined splits) for training, validation, and testing. |
| Hardware Specification | No | The paper does not provide specific hardware details (exact GPU/CPU models, processor types, or memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific ancillary software details, such as library or solver names with version numbers, needed to replicate the experiments. |
| Experiment Setup | Yes | The hyper-parameters of SW-UCB have been set according to the original paper (Garivier & Moulines, 2011) and then optimized to get the smaller regret upper bound. Instead, the hyper-parameters of R-UCB have been fixed for all experiments, using ϵ = 0.1 and α = 3. |