Online Paging with a Vanishing Regret

• URL: http://arxiv.org/abs/2011.09439v2
• Date: Thu, 19 Nov 2020 02:18:08 GMT
• Title: Online Paging with a Vanishing Regret
• Authors: Yuval Emek, Shay Kutten, Yangguang Shi
• Abstract summary: This paper considers a variant of the online paging problem, where the online algorithm has access to multiple predictors, each producing a sequence of predictions for the page arrival times. The predictors may have occasional prediction errors and it is assumed that at least one of them makes a sublinear number of prediction errors in total. Our main result states that this assumption suffices for the design of a randomized online algorithm whose time-average regret with respect to the optimal offline algorithm tends to zero as the time tends to infinity.
• Score: 6.520803851931362
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper considers a variant of the online paging problem, where the online algorithm has access to multiple predictors, each producing a sequence of predictions for the page arrival times. The predictors may have occasional prediction errors and it is assumed that at least one of them makes a sublinear number of prediction errors in total. Our main result states that this assumption suffices for the design of a randomized online algorithm whose time-average regret with respect to the optimal offline algorithm tends to zero as the time tends to infinity. This holds (with different regret bounds) for both the full information access model, where in each round, the online algorithm gets the predictions of all predictors, and the bandit access model, where in each round, the online algorithm queries a single predictor. While online algorithms that exploit inaccurate predictions have been a topic of growing interest in the last few years, to the best of our knowledge, this is the first paper that studies this topic in the context of multiple predictors for an online problem with unbounded request sequences. Moreover, to the best of our knowledge, this is also the first paper that aims for (and achieves) online algorithms with a vanishing regret for a classic online problem under reasonable assumptions.

Related papers

• Learning-Augmented Algorithms with Explicit Predictors [67.02156211760415]
  Recent advances in algorithmic design show how to utilize predictions obtained by machine learning models from past and present data. Prior research in this context was focused on a paradigm where the predictor is pre-trained on past data and then used as a black box. In this work, we unpack the predictor and integrate the learning problem it gives rise for within the algorithmic challenge.
  arXiv  Detail & Related papers  (2024-03-12T08:40:21Z)
• Paging with Succinct Predictions [25.959849403994202]
  We study learning-augmented paging from the new perspective of requiring the least possible amount of predicted information. We develop algorithms for each of two setups that satisfy all three desirable properties of learning-augmented algorithms.
  arXiv  Detail & Related papers  (2022-10-06T09:26:34Z)
• Online TSP with Predictions [3.8411077568039866]
  We study the classical online traveling salesman problem (OLTSP) Unlike the prediction models in other previous studies, each actual request in the OLTSP, associated with its arrival time and position, may not coincide with the predicted ones. Our main result is to study different prediction models and design algorithms to improve the best-known results in the different settings.
  arXiv  Detail & Related papers  (2022-06-30T15:35:53Z)
• A Universal Error Measure for Input Predictions Applied to Online Graph Problems [57.58926849872494]
  We introduce a novel measure for quantifying the error in input predictions. The measure captures errors due to absent predicted requests as well as unpredicted actual requests.
  arXiv  Detail & Related papers  (2022-05-25T15:24:03Z)
• Online Algorithms with Multiple Predictions [17.803569868141647]
  This paper studies online algorithms augmented with multiple machine-learned predictions. Our algorithm incorporates the use of predictions in the classic potential-based analysis of online algorithms.
  arXiv  Detail & Related papers  (2022-05-08T17:33:01Z)
• Memory Bounds for the Experts Problem [53.67419690563877]
  Online learning with expert advice is a fundamental problem of sequential prediction. The goal is to process predictions, and make a prediction with the minimum cost. An algorithm is judged by how well it does compared to the best expert in the set.
  arXiv  Detail & Related papers  (2022-04-21T01:22:18Z)
• Non-Clairvoyant Scheduling with Predictions Revisited [77.86290991564829]
  In non-clairvoyant scheduling, the task is to find an online strategy for scheduling jobs with a priori unknown processing requirements. We revisit this well-studied problem in a recently popular learning-augmented setting that integrates (untrusted) predictions in algorithm design. We show that these predictions have desired properties, admit a natural error measure as well as algorithms with strong performance guarantees.
  arXiv  Detail & Related papers  (2022-02-21T13:18:11Z)
• Robustification of Online Graph Exploration Methods [59.50307752165016]
  We study a learning-augmented variant of the classical, notoriously hard online graph exploration problem. We propose an algorithm that naturally integrates predictions into the well-known Nearest Neighbor (NN) algorithm.
  arXiv  Detail & Related papers  (2021-12-10T10:02:31Z)
• Learning-Augmented Algorithms for Online Steiner Tree [14.506230077020994]
  This paper considers algorithms that predict which terminal arrives online. The predictions may be incorrect and the algorithms' performance is parameterized by the number of incorrectly predicted terminals. We show that the new online algorithms have strong performance even with modestly correct predictions.
  arXiv  Detail & Related papers  (2021-12-10T06:18:19Z)
• Double Coverage with Machine-Learned Advice [100.23487145400833]
  We study the fundamental online $k$-server problem in a learning-augmented setting. We show that our algorithm achieves for any k an almost optimal consistency-robustness tradeoff.
  arXiv  Detail & Related papers  (2021-03-02T11:04:33Z)
• The Primal-Dual method for Learning Augmented Algorithms [10.2730668356857]
  We extend the primal-dual method for online algorithms to incorporate predictions that advise the online algorithm about the next action to take. We show that our algorithms outperform any online algorithm when the prediction is accurate while maintaining good guarantees when the prediction is misleading.
  arXiv  Detail & Related papers  (2020-10-22T11:58:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.