Graph-Triggered Rising Bandits
Authors: Gianmarco Genalti, Marco Mussi, Nicola Gatti, Marcello Restelli, Matteo Castiglioni, Alberto Maria Metelli
ICML 2024 | Conference PDF | Archive PDF | Plain Text | 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 <gianmarco.genalti@polimi.it>. |
| 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. |