Algorithms for Caching and MTS with reduced number of predictions
Code Of Ethics: I acknowledge that I and all co-authors of this work have read and commit to adhering to the ICLR Code of Ethics.
Keywords: ML-Augmented Algorithms, Caching, Metrical Task Systems
Submission Guidelines: I certify that this submission complies with the submission instructions as described on https://iclr.cc/Conferences/2024/AuthorGuide.
TL;DR: We present 1-consistent, smooth, and robust algorithm for caching and consistent, smooth and robust algorithm for MTS with limited access to predictor
Abstract: ML-augmented algorithms utilize predictions to achieve performance beyond their worst-case bounds. Producing these predictions might be a costly operation – this motivated Im et al. [2022] to introduce the study of algorithms which use predictions parsimoniously. We design parsimonious algorithms for caching and MTS with action predictions, proposed by Antoniadis et al. [2023], focusing on the parameters of consistency (performance with perfect predictions) and smoothness (dependence of their performance on prediction error). Our algorithm for caching is 1-consistent, robust, and its smoothness deteriorates with decreasing number of available predictions. We propose an algorithm for general MTS whose consistency and smoothness both scale linearly with the decreasing number of predictions. Without restriction on the number of available predictions, both algorithms match the earlier guarantees achieved by Antoniadis et al. [2023].
Anonymous Url: I certify that there is no URL (e.g., github page) that could be used to find authors' identity.
No Acknowledgement Section: I certify that there is no acknowledgement section in this submission for double blind review.
Primary Area: learning theory
Submission Number: 5029
Loading