An $\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits
Authors: Geovani Rizk, Igor Colin, Albert Thomas, Rida Laraki, Yann Chevaleyre
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we show through various experiments the validity of our approach. and Section 5 gives an experimental overview of the various theoretical bounds established for the regret. |
| Researcher Affiliation | Collaboration | Geovani Rizk PSL Université Paris Dauphine, CNRS, LAMSADE, Paris, France... Igor Colin Huawei Noah s Ark Lab, Paris, France... Albert Thomas Huawei Noah s Ark Lab, Paris, France... Rida Laraki PSL Université Paris Dauphine, CNRS, LAMSADE, Paris, France... Yann Chevaleyre PSL Université Paris Dauphine, CNRS, LAMSADE, Paris, France |
| Pseudocode | Yes | Algorithm 1: Adaptation of OFUL algorithm for Graphical Bilinear Bandit, Algorithm 2: Approx-MAX-CUT, Algorithm 3: Improved OFUL for Graphical Bilinear Bandits |
| Open Source Code | No | Did you include the code, data, and instructions needed to reproduce the main experimental results (either in the supplemental material or as a URL)? [No] The code will be released if the paper is accepted to the conference |
| Open Datasets | No | No concrete access information (specific link, DOI, repository name, formal citation with authors/year, or reference to established benchmark datasets) for a publicly available or open dataset was found. The paper mentions "synthetic datasets" but provides no access details. |
| Dataset Splits | No | No specific dataset split information (exact percentages, sample counts, citations to predefined splits, or detailed splitting methodology) needed to reproduce the data partitioning was found. |
| Hardware Specification | No | Did you include the total amount of compute and the type of resources used (e.g., type of GPUs, internal cluster, or cloud provider)? [Yes] Also in the appendix. (The main paper text provided does not contain these details.) |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers like Python 3.8, CPLEX 12.4) needed to replicate the experiment were found in the main paper. |
| Experiment Setup | Yes | Experiments were performed on graphs of n = 100 nodes, and results for the random graph are averaged over 100 draws... The dimension d of the arm-set is 10 (which gives linear reward with unknown parameter ? of dimension 100). The plotted curve represents the average value of the parameters over 100 different matrices M? initiated randomly with positive values... We use a complete graph of 5 nodes, we run the experiment on 5 different matrices as in Figure 1 with = 0 and run it 10 different times to plot the average fraction of the global reward. |