Fast Rates for Bandit Optimization with Upper-Confidence Frank-Wolfe
Authors: Quentin Berthet, Vianney Perchet
NeurIPS 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| 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 q.berthet@statslab.cam.ac.uk Vianney Perchet ENS Paris-Saclay & Criteo Research, Paris vianney.perchet@normalesup.org |
| 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. |