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A recent line of research investigates how algorithms can be augmented with machine-learned predictions to overcome worst case lower bounds.
How much data is sufficient to learn high-performing algorithms?
Maria-Florina Balcan, Dan F. DeBlasio, Travis Dick, Carl Kingsford, Tuomas Sandholm, and Ellen Vitercik · 1908
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Learning optimal search algorithms from data
Shuchi Chawla, Evangelia Gergatsouli, Yifeng Teng, Christos Tzamos, and Ruimin Zhang · 1911
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An n 5 / 2 n^{5/2} -algorithm for maximum matchings in bipartite graphs
John E. Hopcroft and Richard M. Karp · 1973
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A scaling algorithm for weighted matching on general graphs
Harold N. Gabow · 1985
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New scaling algorithms for the assignment and minimum mean cycle problems
James B. Orlin and Ravindra K. Ahuja · 1992
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A faster strongly polynomial minimum cost flow algorithm
James B. Orlin · 1993
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A faster deterministic maximum flow algorithm
V. King, S. Rao, and R. Tarjan · 1994
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Algorithms for dense graphs and networks on the random access computer
Joseph Cheriyan and Kurt Mehlhorn · 1996
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Global price updates help
Andrew V. Goldberg and Robert Kennedy · 1997
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Warm start of the primal-dual method applied in the cutting-plane scheme
Jacek Gondzio · 1998
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A simple approximation algorithm for the weighted matching problem
Doratha E. Drake and Stefan Hougardy · 2003
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Composable sketches for functions of frequencies: Beyond the worst case
Edith Cohen, Ofir Geri, and Rasmus Pagh · 2004
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Michael Mitzenmacher and Sergei Vassilvitskii · 2006
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Weighted bipartite matching in matrix multiplication time
Piotr Sankowski · 2006
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Partitioned learned bloom filter
Kapil Vaidya, Eric Knorr, Tim Kraska, and Michael Mitzenmacher · 2006
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Adwords and generalized online matching
Aranyak Mehta, Amin Saberi, Umesh V. Vazirani, and Vijay V. Vazirani · 2007
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Faster dynamic matchings and vertex connectivity
Piotr Sankowski · 2007
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Faster approximate lossy generalized flow via interior point algorithms
Samuel I. Daitch and Daniel A. Spielman · 2008
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Neural network learning: Theoretical foundations
Martin Anthony and Peter L Bartlett · 2009
Cited alongside, same era.
The adwords problem: online keyword matching with budgeted bidders under random permutations
Nikhil R. Devanur and Thomas P. Hayes · 2009
Cited alongside, same era.
Optimal online assignment with forecasts
Erik Vee, Sergei Vassilvitskii, and Jayavel Shanmugasundaram · 2010
Cited alongside, same era.
A primal-dual exterior point method for nonlinear optimization
Hiroshi Yamashita and Takahito Tanabe · 2010
Cited alongside, same era.
Paul Dütting, Silvio Lattanzi, Renato Paes Leme, and Sergei Vassilvitskii · 2011
Cited alongside, same era.
Skin segmentation dataset, uci machine learning repository, 2012
Rajen Bhatt and Abhinav Dhall · 2012
Dispersion for data-driven algorithm design, online learning, and private optimization
Maria-Florina Balcan, Travis Dick, and Ellen Vitercik · 2018
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Data-driven clustering via parameterized lloyd’s families
Maria-Florina Balcan, Travis Dick, and Colin White · 2018
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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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Competitive caching with machine learned advice
Thodoris Lykouris and Sergei Vassilvitskii · 2018
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A model for learned bloom filters and optimizing by sandwiching
Michael Mitzenmacher · 2018
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Learning fast optimizers for contextual stochastic integer programs
Vinod Nair, Dj Dvijotham, Iain Dunning, and Oriol Vinyals · 2018
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A scaling algorithm for maximum weight matching in bipartite graphs
Ran Duan and Hsin - Hao Su · 2012
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Convergence of stochastic processes
David Pollard · 2012
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Max flows in o(nm) time, or better
James B. Orlin · 2013
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Feedback prediction for blogs
Krisztian Buza · 2014
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Linear-time approximation for maximum weight matching
Ran Duan and Seth Pettie · 2014
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Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow
Yin Tat Lee and Aaron Sidford · 2014
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Improving online algorithms via ml predictions
Manish Purohit, Zoya Svitkina, and Ravi Kumar · 2018
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(learned) frequency estimation algorithms under zipfian distribution
Anders Aamand, Piotr Indyk, and Ali Vakilian · 2019
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Learning-based frequency estimation algorithms
Chen-Yu Hsu, Piotr Indyk, Dina Katabi, and Ali Vakilian · 2019
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Customizing ML predictions for online algorithms
Keerti Anand, Rong Ge, and Debmalya Panigrahi · 2020
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Online metric algorithms with untrusted predictions
Antonios Antoniadis, Christian Coester, Marek Eliás, Adam Polak, and Bertrand Simon · 2020
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The primal-dual method for learning augmented algorithms
Étienne Bamas, Andreas Maggiori, and Ola Svensson · 2020
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Online algorithms for weighted paging with predictions
Zhihao Jiang, Debmalya Panigrahi, and Kevin Sun · 2020
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Online scheduling via learned weights
Silvio Lattanzi, Thomas Lavastida, Benjamin Moseley, and Sergei Vassilvitskii · 2020
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Near-optimal bounds for online caching with machine learned advice
Dhruv Rohatgi · 2020
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Beyond the Worst-Case Analysis of Algorithms
Tim Roughgarden · 2020
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Bipartite matching in nearly-linear time on moderately dense graphs
Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, and Di Wang · 2020
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