Leveraging Good Representations in Linear Contextual Bandits
Authors: Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
ICML 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we empirically validate our theoretical findings in a number of standard contextual bandit problems. |
| Researcher Affiliation | Collaboration | 1Politecnico di Milano, Milan, Italy 2Facebook AI Research, Paris, France. |
| Pseudocode | Yes | Algorithm 1 LEADER Algorithm |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the described methodology. |
| Open Datasets | Yes | Jester Dataset (Goldberg et al., 2001), which consists of joke ratings in a continuous range from 10 to 10 for a total of 100 jokes and 73421 users. [...] Last.fm dataset (Cantador et al., 2011). |
| Dataset Splits | No | The paper does not explicitly state specific train/validation/test dataset splits needed for reproduction. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU, GPU models, or memory specifications) used for running its experiments. |
| Software Dependencies | No | The paper does not list specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | All results are averaged over 20 independent runs, with shaded areas corresponding to 2 standard deviations. We always set the parameters to λ = 1, δ = 0.01, and σ = 0.3. All the representations we consider are normalized to have θ i = 1. |