Online Dynamic Programming
Authors: Holakou Rahmanian, Manfred K. K. Warmuth
NeurIPS 2017 | Conference 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 holakou@ucsc.edu Manfred K. Warmuth Department of Computer Science University of California Santa Cruz Santa Cruz, CA 95060 manfred@ucsc.edu |
| 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. |