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We propose a framework for speeding up maximum flow computation by using predictions.
Maximal flow through a network
L. R. Ford and D. R. Fulkerson · 1956
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Probability inequalities for sums of bounded random variables
Wassily Hoeffding · 1963
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Algorithm for solution of a problem of maximum flow in networks with power estimation
Yefim Dinitz · 1970
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Theoretical improvements in algorithmic efficiency for network flow problems
Jack R. Edmonds and Richard M. Karp · 1972
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Convergence of Stochastic Processes
David Pollard · 1984
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A new approach to the maximum flow problem
Andrew V. Goldberg and Robert Endre Tarjan · 1986
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Network flows – theory, algorithms and applications
Ravindra K. Ahuja, Thomas L. Magnanti, and James B. Orlin · 1993
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Almost linear VC dimension bounds for piecewise polynomial networks
Peter L. Bartlett, Vitaly Maiorov, and Ron Meir · 1998
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Neural Network Learning – Theoretical Foundations
Martin Anthony and Peter L. Bartlett · 2002
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An experimental comparison of min-cut/max-flow algorithms for energy minimization in vision
Yuri Boykov and Vladimir Kolmogorov · 2004
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Nearly-linear time algorithms for graph partitioning, graph sparsification, and solving linear systems
Daniel A. Spielman and Shang-Hua Teng · 2004
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Efficiently solving dynamic markov random fields using graph cuts
Pushmeet Kohli and Philip H. S. Torr · 2005
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Active graph cuts
Olivier Juan and Yuri Boykov · 2006
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Allocating online advertisement space with unreliable estimates
Mohammad Mahdian, Hamid Nazerzadeh, and Amin Saberi · 2007
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The pseudoflow algorithm: A new algorithm for the maximum-flow problem
Dorit S. Hochbaum · 2008
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The case for learned index structures
Tim Kraska, Alex Beutel, Ed H. Chi, Jeffrey Dean, and Neoklis Polyzotis · 2018
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Improving online algorithms via ML predictions
Manish Purohit, Zoya Svitkina, and Ravi Kumar · 2018
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Learning-based frequency estimation algorithms
Chen-Yu Hsu, Piotr Indyk, Dina Katabi, and Ali Vakilian · 2019
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Algorithms with predictions
Michael Mitzenmacher and Sergei Vassilvitskii · 2020
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Faster matchings via learned duals
Michael Dinitz, Sungjin Im, Thomas Lavastida, Benjamin Moseley, and Sergei Vassilvitskii · 2021
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Learning-based support estimation in sublinear time
Talya Eden, Piotr Indyk, Shyam Narayanan, Ronitt Rubinfeld, Sandeep Silwal, and Tal Wagner · 2021
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Understanding Machine Learning – From Theory to Algorithms
Shai Shalev-Shwartz and Shai Ben-David · 2014
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On the pseudo-dimension of nearly optimal auctions
Jamie Morgenstern and Tim Roughgarden · 2015
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A competitive study of the pseudoflow algorithm for the minimum s-t cut problem in vision applications
Barak Fishbain, Dorit S. Hochbaum, and Stefan Müller · 2016
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Competitive caching with machine learned advice
Thodoris Lykouris and Sergei Vassilvitskii · 2021
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Faster fundamental graph algorithms via learned predictions
Justin Y. Chen, Sandeep Silwal, Ali Vakilian, and Fred Zhang · 2022
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Maximum flow and minimum-cost flow in almost-linear time, 2022
Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, and Sushant Sachdeva · 2022
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