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.
Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Authors: Quentin Berthet, Vianney Perchet
NeurIPS 2017 | Venue PDF | LLM Run Details | Input Tokens: 15,720 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 2,221 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 | We give theoretical guarantees for the performance of this algorithm over various classes of functions, and discuss the optimality of these results. |
| Researcher Affiliation | Collaboration | Quentin Berthet University of Cambridge EMAIL Vianney Perchet ENS Paris-Saclay & Criteo Research, Paris EMAIL |
| Pseudocode | Yes | Algorithm 0: UCB Frank-Wolfe algorithm |
| Open Source Code | No | The paper does not provide any statement or link for open-source code related to its methodology. |
| Open Datasets | No | The paper is theoretical and does not report on experiments using datasets for training. |
| Dataset Splits | No | The paper is theoretical and does not report on experiments using datasets, thus no validation splits are mentioned. |
| Hardware Specification | No | The paper does not report on experiments, thus no hardware specifications are provided. |
| Software Dependencies | No | The paper does not report on experiments, thus no software dependencies with version numbers are mentioned. |
| Experiment Setup | No | The paper does not report on experiments, thus no experimental setup details like hyperparameters or system-level training settings are provided. |