Online Dynamic Programming
Authors: Holakou Rahmanian, Manfred K. K. Warmuth
NeurIPS 2017 | Venue PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop a general methodology for tackling such problems for a wide class of dynamic programming algorithms. Our framework allows us to extend online learning algorithms like Hedge [16] and Component Hedge [25] to a significantly wider class of combinatorial objects than was possible before. |
| Researcher Affiliation | Academia | Holakou Rahmanian Department of Computer Science University of California Santa Cruz Santa Cruz, CA 95060 EMAIL Manfred K. Warmuth Department of Computer Science University of California Santa Cruz Santa Cruz, CA 95060 EMAIL |
| Pseudocode | No | The paper describes algorithms in prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | No | The paper is theoretical and does not use datasets for empirical evaluation. Therefore, it does not provide information about public dataset access for training. |
| Dataset Splits | No | The paper focuses on theoretical contributions and does not include empirical experiments with dataset splits for training, validation, or testing. |
| Hardware Specification | No | The paper describes theoretical algorithms and does not report on empirical experiments, so no hardware specifications are mentioned. |
| Software Dependencies | No | The paper is theoretical and does not report on empirical experiments, so no specific software dependencies with version numbers are listed. |
| Experiment Setup | No | The paper describes theoretical algorithms and does not report on empirical experiments, so no experimental setup details like hyperparameters or training configurations are provided. |