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 Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
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. |