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Learned Predictions for Data Structures and Running Time (Dagstuhl Seminar 25181)

Authors Inge Li Gørtz, Benjamin J. Moseley, Shikha Singh, Sergei Vassilvitskii and all authors of the abstracts in this report

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DagRep.15.4.112.pdf
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Document Identifiers

Subject Classification

ACM Subject Classification
  • Theory of computation
Keywords
  • algorithms with predictions
  • approximation algorithms
  • beyond-worst-case analysis
  • data structures
  • learning-augmented algorithms

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Abstract

This report documents the program and the outcomes of Dagstuhl Seminar 25181 "Learned Predictions for Data Structures and Running Time". The focus of the seminar was applying the new algorithms-with-predictions framework to improve worst-case running time of algorithms and data structures. 
This seminar brought together researchers from the data structures, combinatorial optimization and learned predictions communities to address the challenges of adopting learned machine-learned predictions for improving running time guarantees.

Cite As Get BibTex

Inge Li Gørtz, Benjamin J. Moseley, Shikha Singh, and Sergei Vassilvitskii. Learned Predictions for Data Structures and Running Time (Dagstuhl Seminar 25181). In Dagstuhl Reports, Volume 15, Issue 4, pp. 112-125, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2025) https://doi.org/10.4230/DagRep.15.4.112

Author Details

Inge Li Gørtz
  • Technical University of Denmark - Lyngby, DK
Benjamin J. Moseley
  • Carnegie Mellon University - Pittsburgh, US
Shikha Singh
  • Williams College - Williamstown, US
Sergei Vassilvitskii
  • Google - New York, US
and all authors of the abstracts in this report
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