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From Distributed Algorithms to Machine Learning and Back

Published: 16 June 2023 Publication History

Abstract

In the realm of computer science, it may seem that distributed computing and machine learning exist on opposite ends of the spectrum. However, there are many connections between the two domains, both in theory and practice.
Recently, machine learning research has become excited about graphs. And when machine learning meets graphs, researchers familiar with distributed algorithms may experience a sense of déjà vu, as many classic distributed computing paradigms are being rediscovered. It feels a bit like "machine learning + graphs = distributed algorithms." In my talk, I am going to introduce some key concepts in graph machine learning such as underreaching and oversquashing. These concepts have been known in the distributed computing community as local and congest, respectively.
In the main part of the talk, I am going to present some recent breakthroughs in this exciting intersection of fields. Finally, I will also present some intriguing open problems.

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Published In

cover image ACM Conferences
PODC '23: Proceedings of the 2023 ACM Symposium on Principles of Distributed Computing
June 2023
392 pages
ISBN:9798400701214
DOI:10.1145/3583668
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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 16 June 2023

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Author Tags

  1. graph neural networks
  2. distributed computing
  3. networks algorithms

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  • Invited-talk

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PODC '23
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PODC '23 Paper Acceptance Rate 29 of 110 submissions, 26%;
Overall Acceptance Rate 740 of 2,477 submissions, 30%

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