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].
Multi-Armed Bandits with Metric Movement Costs
Authors: Tomer Koren, Roi Livni, Yishay Mansour
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | Our main contribution gives a tight characterization of the expected minimax regret in this setting, in terms of a complexity measure C of the underlying metric which depends on its covering numbers. In finite metric spaces with k actions, we give an efficient algorithm that achieves regret of the form e O(max{C1/3T2/3, k T}), and show that this is the best possible. |
| Researcher Affiliation | Collaboration | Tomer Koren Google Brain EMAIL Roi Livni Princeton University EMAIL Yishay Mansour Tel Aviv University and Google EMAIL |
| Pseudocode | Yes | Algorithm 1: The SMB algorithm. |
| Open Source Code | No | The paper does not provide explicit access to source code for the methodology described. |
| Open Datasets | No | This is a theoretical paper that does not describe empirical experiments using specific datasets for training. |
| Dataset Splits | No | This is a theoretical paper that does not report on empirical experiments requiring dataset splits for validation. |
| Hardware Specification | No | The paper is theoretical and does not mention any specific hardware used for experiments. |
| Software Dependencies | No | The paper is theoretical and does not list any specific software dependencies with version numbers. |
| Experiment Setup | No | The paper is theoretical and does not describe an experimental setup with hyperparameters or training configurations. |