The Primal-Dual method for Learning Augmented Algorithms

• URL: http://arxiv.org/abs/2010.11632v1
• Date: Thu, 22 Oct 2020 11:58:47 GMT
• Title: The Primal-Dual method for Learning Augmented Algorithms
• Authors: \'Etienne Bamas, Andreas Maggiori, Ola Svensson
• Abstract summary: 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.
• Score: 10.2730668356857
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The extension of classical online algorithms when provided with predictions is a new and active research area. In this paper, we extend the primal-dual method for online algorithms in order to incorporate predictions that advise the online algorithm about the next action to take. We use this framework to obtain novel algorithms for a variety of online covering problems. We compare our algorithms to the cost of the true and predicted offline optimal solutions and show that these algorithms outperform any online algorithm when the prediction is accurate while maintaining good guarantees when the prediction is misleading.

Related papers

• Improving Online Algorithms via ML Predictions [19.03466073202238]
  We consider two classical problems, ski rental and non-clairvoyant job scheduling, and obtain new online algorithms that use predictions to make their decisions. These algorithms are oblivious to the performance of the predictor, improve with better predictions, but do not degrade much if the predictions are poor.
  arXiv  Detail & Related papers  (2024-07-25T02:17:53Z)
• A Simple Learning-Augmented Algorithm for Online Packing with Concave Objectives [4.9826534303287335]
  We introduce and analyze a simple learning-augmented algorithm for online packing problems with linear constraints and concave objectives. We further raise the problem of understanding necessary and sufficient conditions for when such simple black-box solutions may be optimal.
  arXiv  Detail & Related papers  (2024-06-05T18:39:28Z)
• 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)
• Algorithms with Prediction Portfolios [23.703372221079306]
  We study the use of multiple predictors for a number of fundamental problems, including matching, load balancing, and non-clairvoyant scheduling. For each of these problems we introduce new algorithms that take advantage of multiple predictors, and prove bounds on the resulting performance.
  arXiv  Detail & Related papers  (2022-10-22T12:58:07Z)
• 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)
• 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)
• Learning Predictions for Algorithms with Predictions [49.341241064279714]
  We introduce a general design approach for algorithms that learn predictors. We apply techniques from online learning to learn against adversarial instances, tune robustness-consistency trade-offs, and obtain new statistical guarantees. We demonstrate the effectiveness of our approach at deriving learning algorithms by analyzing methods for bipartite matching, page migration, ski-rental, and job scheduling.
  arXiv  Detail & Related papers  (2022-02-18T17:25:43Z)
• 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)
• 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)
• Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms [85.97516436641533]
  We study the problem of improving the performance of online algorithms by incorporating machine-learned predictions. The goal is to design algorithms that are both consistent and robust. We provide the first set of non-trivial lower bounds for competitive analysis using machine-learned predictions.
  arXiv  Detail & Related papers  (2020-10-22T04:51:01Z)

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.