Learning-Augmented Algorithms with Explicit Predictors
Authors: Marek Elias, Haim Kaplan, Yishay Mansour, Shay Moran
NeurIPS 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | This is a theoretical work and we state our results including all assumptions required for their validity. |
| Researcher Affiliation | Collaboration | Marek Eliáš Bocconi University, Milan Haim Kaplan Tel Aviv University Google Research Yishay Mansour Tel Aviv University Google Research Shay Moran Department of Mathematics, Technion Department of Computer Science, Technion Department of Data and Decision Sciences, Technion Google Research. |
| Pseudocode | Yes | Algorithm 2: caching, realizable setting |
| Open Source Code | No | This work does not release any code nor data. |
| Open Datasets | No | The paper is a theoretical work and does not use specific, named datasets for training. It refers to 'historical data' and 'past input instances' in an abstract sense for its theoretical framework, but does not provide concrete access information for any dataset. |
| Dataset Splits | No | This paper is theoretical and does not involve empirical evaluation with data. Therefore, it does not specify training, validation, or test dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not include any empirical experiments; therefore, it does not specify hardware used. |
| Software Dependencies | No | This paper is theoretical and does not include any empirical experiments; therefore, it does not specify software dependencies. |
| Experiment Setup | No | This paper is theoretical and does not include any empirical experiments; therefore, it does not provide details on experimental setup or hyperparameters. |