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..
Leveraging Good Representations in Linear Contextual Bandits
Authors: Matteo Papini, Andrea Tirinzoni, Marcello Restelli, Alessandro Lazaric, Matteo Pirotta
ICML 2021 | Venue PDF | 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. |