Fetching the paper…
Reading the bibliography…
Sorting an array is a fundamental routine in machine learning, one that is used to compute rank-based statistics, cumulative distribution functions (CDFs), quantiles, or to select closest neighbors and labels.
Tres observaciones sobre el algebra lineal
Garrett Birkhoff · 1946
Earlier work this paper cites.
The use of entropy maximizing models, in the theory of trip distribution, mode split and route split
Alan Geoffrey Wilson · 1969
Earlier work this paper cites.
Least median of squares regression
Peter J Rousseeuw · 1984
Earlier work this paper cites.
On the scaling of multidimensional matrices
Joel Franklin and Jens Lorenz · 1989
Earlier work this paper cites.
The invisible hand algorithm: Solving the assignment problem with statistical physics
JJ Kosowsky and Alan L Yuille · 1994
Earlier work this paper cites.
Introduction to Linear Optimization
Dimitris Bertsimas and John N Tsitsiklis · 1997
Earlier work this paper cites.
Dynamic trees as search trees via euler tours, applied to the network simplex algorithm
Robert E. Tarjan · 1997
Earlier work this paper cites.
Cumulated gain-based evaluation of ir techniques
Kalervo Järvelin and Jaana Kekäläinen · 2002
Earlier work this paper cites.
Order statistics
Herbert Aron David and Haikady Navada Nagaraja · 2004
Earlier work this paper cites.
Learning to rank with nonsmooth cost functions
Christopher J Burges, Robert Ragno, and Quoc V Le · 2007
Earlier work this paper cites.
Softrank: optimizing non-smooth rank metrics
Michael Taylor, John Guiver, Stephen Robertson, and Tom Minka · 2008
Earlier work this paper cites.
Matching with trade-offs: revealed preferences over competing characteristics
Alfred Galichon and Bernard Salanié · 2009
Earlier work this paper cites.
Distance metric learning for large margin nearest neighbor classification
Kilian Q Weinberger and Lawrence K Saul · 2009
Earlier work this paper cites.
A general approximation framework for direct optimization of information retrieval measures
Tao Qin, Tie-Yan Liu, and Hang Li · 2010
Cited alongside, same era.
Learning to rank using an ensemble of lambda-gradient models
Christopher Burges, Krysta Svore, Paul Bennett, Andrzej Pastusiak, and Qiang Wu · 2011
Cited alongside, same era.
Robust statistics
Peter J Huber · 2011
Cited alongside, same era.
Wasserstein barycenter and its application to texture mixing
Julien Rabin, Gabriel Peyré, Julie Delon, and Marc Bernot · 2011
Cited alongside, same era.
Accuracy at the top
Stephen Boyd, Corinna Cortes, Mehryar Mohri, and Ana Radovanovic · 2012
Cited alongside, same era.
Local matching indicators for transport problems with concave costs
Julie Delon, Julien Salomon, and Andrei Sobolevski · 2012
Cited alongside, same era.
Optimal transport for applied mathematicians
Filippo Santambrogio · 2015
Later among the works it cites.
Wasserstein barycentric coordinates: histogram regression using optimal transport
Nicolas Bonneel, Gabriel Peyré, and Marco Cuturi · 2016
Later among the works it cites.
Learning population-level diffusions with generative RNNs
Tatsunori Hashimoto, David Gifford, and Tommi Jaakkola · 2016
Later among the works it cites.
Sliced Wasserstein kernels for probability distributions
Soheil Kolouri, Yang Zou, and Gustavo K Rohde · 2016
Later among the works it cites.
Stabilized sparse scaling algorithms for entropy regularized transport problems
Bernhard Schmitzer · 2016
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Rearrangement, convection, convexity and entropy
Yann Brenier · 2013
Cited alongside, same era.
Sinkhorn distances: lightspeed computation of optimal transport
Marco Cuturi · 2013
Cited alongside, same era.
Algorithms for direct 0–1 loss optimization in binary classification
Tan Nguyen and Scott Sanner · 2013
Cited alongside, same era.
Direct 0-1 loss minimization and margin maximization with boosting
Shaodan Zhai, Tian Xia, Ming Tan, and Shaojun Wang · 2013
Cited alongside, same era.
Fast computation of Wasserstein barycenters
Marco Cuturi and Arnaud Doucet · 2014
Cited alongside, same era.
Sliced and Radon Wasserstein barycenters of measures
Nicolas Bonneel, Julien Rabin, Gabriel Peyré, and Hanspeter Pfister · 2015
Cited alongside, same era.
Leonard Berrada, Andrew Zisserman, and M Pawan Kumar · 2018
Later among the works it cites.
Wasserstein discriminant analysis
Rémi Flamary, Marco Cuturi, Nicolas Courty, and Alain Rakotomamonjy · 2018
Later among the works it cites.
Stochastic optimization of sorting networks via continuous relaxation
Aditya Grover, Eric Wang, Aaron Zweig, and Stefano Ermon · 2019
Closest in time.
Wasserstein fair classification
Ray Jiang, Aldo Pacchiano, Tom Stepleton, Heinrich Jiang, and Silvia Chiappa · 2019
Closest in time.
Robust machine learning by median-of-means: theory and practice
Guillaume Lecué and Matthieu Lerasle · 2019
Closest in time.
Regularization, sparse recovery, and median-of-means tournaments
Gábor Lugosi, Shahar Mendelson, et al · 2019
Closest in time.
Computational optimal transport
Gabriel Peyré and Marco Cuturi · 2019
Closest in time.
Conformalized quantile regression
Yaniv Romano, Evan Patterson, and Emmanuel J Candès · 2019
Closest in time.