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Constrained competitive optimization involves multiple agents trying to minimize conflicting objectives, subject to constraints.
Extragradient method for finding saddle points and other problems
GM Korpelevich · 1977
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Problem complexity and method efficiency in optimization
Arkadi Semenovich Nemirovsky and David Borisovich Yudin · 1983
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Fast exact multiplication by the hessian
Barak A Pearlmutter · 1994
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Convex analysis and variational problems
Ivar Ekeland and Roger Temam · 1999
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On the convergence properties of the projected gradient method for convex optimization
Alfredo N Iusem · 2003
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Finite-dimensional variational inequalities and complementarity problems. Vol. II
Francisco Facchinei and Jong-Shi Pang · 2003
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Iterative methods for sparse linear systems
Yousef Saad · 2003
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Methods of information geometry
Shun-ichi Amari and Hiroshi Nagaoka · 2007
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Generative adversarial nets
Ian Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-Farley, Sherjil Ozair, Aaron Courville, and Yoshua Bengio · 2014
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The information geometry of mirror descent
Garvesh Raskutti and Sayan Mukherjee · 2015
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Group equivariant convolutional networks
Taco Cohen and Max Welling · 2016
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Information geometry and its applications
Shun-ichi Amari · 2016
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Label-free supervision of neural networks with physics and domain knowledge
Russell Stewart and Stefano Ermon · 2017
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Constrained policy optimization
Joshua Achiam, David Held, Aviv Tamar, and Pieter Abbeel · 2017
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2017
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The numerics of gans
Lars Mescheder, Sebastian Nowozin, and Andreas Geiger · 2017
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Global convergence to the equilibrium of gans using variational inequalities
Ian Gemp and Sridhar Mahadevan · 2018
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Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations
Maziar Raissi, Paris Perdikaris, and George E Karniadakis · 2019
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Cormorant: Covariant molecular neural networks
Brandon Anderson, Truong Son Hy, and Risi Kondor · 2019
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Reinforcement learning with convex constraints
Sobhan Miryoosefi, Kianté Brantley, Hal Daume III, Miro Dudik, and Robert E Schapire · 2019
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A lagrangian method for inverse problems in reinforcement learning
Pierre-Luc Bacon, Florian Schäfer, Clement Gehring, Animashree Anandkumar, and Emma Brunskill · 2019
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Training well-generalizing classifiers for fairness metrics and other data-dependent constraints
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Two-player games for efficient non-convex constrained optimization
Andrew Cotter, Heinrich Jiang, and Karthik Sridharan · 2018
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The mechanics of n-player differentiable games
David Balduzzi, Sebastien Racaniere, James Martens, Jakob Foerster, Karl Tuyls, and Thore Graepel · 2018
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Competitive gradient descent
Florian Schäfer and Anima Anandkumar · 2019
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Implicit competitive regularization in gans
Florian Schäfer, Hongkai Zheng, and Anima Anandkumar · 2019
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Differentiable game mechanics
Alistair Letcher, David Balduzzi, Sébastien Racaniere, James Martens, Jakob N Foerster, Karl Tuyls, and Thore Graepel · 2019
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