Refined Lower Bounds for Adversarial Bandits
Authors: Sébastien Gerchinovitz, Tor Lattimore
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We provide new lower bounds on the regret that must be suffered by adversarial bandit algorithms. The new results show that recent upper bounds that either (a) hold with high-probability or (b) depend on the total loss of the best arm or (c) depend on the quadratic variation of the losses, are close to tight. Besides this we prove two impossibility results. |
| Researcher Affiliation | Academia | Sébastien Gerchinovitz Institut de Mathématiques de Toulouse Université Toulouse 3 Paul Sabatier Toulouse, 31062, France sebastien.gerchinovitz@math.univ-toulouse.fr Tor Lattimore Department of Computing Science University of Alberta Edmonton, Canada tor.lattimore@gmail.com |
| Pseudocode | No | The paper does not contain any pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link for open-source code for the described methodology. |
| Open Datasets | No | The paper does not mention using any specific datasets, as it focuses on theoretical bounds and proofs. |
| Dataset Splits | No | The paper does not describe any training, validation, or test dataset splits, as it is a theoretical paper and does not conduct experiments on data. |
| Hardware Specification | No | The paper does not specify any hardware used for experiments, as it focuses on theoretical analysis. |
| Software Dependencies | No | The paper does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper does not provide details about an experimental setup, hyperparameters, or system-level training settings, as it is a theoretical paper. |