Notice: This website is continuously updated as new conference proceedings and journal volumes are published. Consequently, the figures presented here may differ from those reported in our publications.
A brief overview of our methodology is provided on the Methods page, along with references to publications documenting our approach and links to the code and data used in each study.
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.
An Improved Algorithm for Adversarial Linear Contextual Bandits via Reduction
Authors: Tim van Erven, Jack Mayo, Julia Olkhovskaya, Chen-Yu Wei
NeurIPS 2025 | Venue PDF | LLM Run Details | Input Tokens: 22,596 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,421 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theory paper without experiments. This is theoretical work, which did not require human subjects or participants, and involved no data. It contributes to the fundamental understanding of contextual bandits, without any direct foreseeable negative societal impact. Mitigation measures were therefore not applicable. This is theoretical work, which contributes to the fundamental understanding of contextual bandits. |
| Researcher Affiliation | Academia | University of Amsterdam. Email: EMAIL University of Amsterdam; Kurtos.ai. Email: EMAIL Delft University of Technology. Email: EMAIL University of Virginia. Email: EMAIL |
| Pseudocode | Yes | Algorithm 1: Adversarial Linear Contextual Bandits Algorithm 2: Misspecification-Robust Continuous Exponential Weights |
| Open Source Code | No | The paper does not include experiments requiring data or code. |
| Open Datasets | No | This is a theory paper without experiments. |
| Dataset Splits | No | The paper does not include experiments. |
| Hardware Specification | No | The paper does not include experiments. |
| Software Dependencies | No | The paper does not include experiments. |
| Experiment Setup | No | The paper does not include experiments. |