Linear Contextual Bandits With Interference
Authors: Yang Xu, Wenbin Lu, Rui Song
ICML 2025 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The effectiveness of our approach is demonstrated through simulations and synthetic data generated based on Movie Lens data. |
| Researcher Affiliation | Academia | 1Department of Statistics, North Carolina State University, USA. Correspondence to: Rui Song <EMAIL>. |
| Pseudocode | Yes | Algorithm 1 Linear Contextual Bandits with Interference |
| Open Source Code | Yes | All supplementary code is available at our Github repository. |
| Open Datasets | Yes | The Movie Lens 1M dataset (Harper & Konstan, 2015) contains over 1 million movie ratings from 6k users, aiding movie recommendations based on historical ratings. |
| Dataset Splits | No | Based on the timestamps of each rating and the relative user density, we divided the dataset into T = 200 rounds. |
| Hardware Specification | No | All experiments were conducted on a local computer with 16 GB of memory. |
| Software Dependencies | No | No specific software dependencies with version numbers are mentioned in the paper. |
| Experiment Setup | Yes | The simulation setup of testing coverage probability is as follows. In the estimation of β, the entire process is replicated for B = 1000 times to calculate the empirical coverage. For each replication, we assume there are a total of T = 500 rounds, and we randomly sample the true β from β0 = (2, 3, 1) and β1 = (1, 1, 3) . |