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 [1].
Refined Lower Bounds for Adversarial Bandits
Authors: Sébastien Gerchinovitz, Tor Lattimore
NeurIPS 2016 | Venue PDF | 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 EMAIL Tor Lattimore Department of Computing Science University of Alberta Edmonton, Canada EMAIL |
| 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. |