Paging with Succinct Predictions
Authors: Antonios Antoniadis, Joan Boyar, Marek Elias, Lene Monrad Favrholdt, Ruben Hoeksma, Kim S. Larsen, Adam Polak, Bertrand Simon
ICML 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Theoretical | We develop algorithms satisfy all three desirable properties of learning-augmented algorithms that is, they are consistent, robust and smooth despite being limited to a one-bit prediction per request. We also present lower bounds establishing that our algorithms are essentially best possible. |
| Researcher Affiliation | Academia | 1University of Twente, Enschede, Netherlands 2University of Southern Denmark, Odense, Denmark 3Bocconi University, Milan, Italy 4Max Planck Institute for Informatics, Saarbr ucken, Germany 5IN2P3 Computing Center and CNRS, Villeurbanne, France. |
| Pseudocode | Yes | Algorithm 1 MARK0 Eviction Strategy; Algorithm 2 MARK&PREDICT Eviction Strategy |
| Open Source Code | No | This paper is theoretical and does not mention releasing source code. |
| Open Datasets | No | This paper is theoretical and does not use datasets for training. |
| Dataset Splits | No | This paper is theoretical and does not report on experiments requiring dataset splits. |
| Hardware Specification | No | This paper is theoretical and does not describe hardware used for experiments. |
| Software Dependencies | No | This paper is theoretical and does not mention specific software dependencies with version numbers for implementation. |
| Experiment Setup | No | This paper is theoretical and does not include details on experimental setup or hyperparameters. |