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The success of adversarial formulations in machine learning has brought renewed motivation for smooth games.
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Mini-batch semi-stochastic gradient descent in the proximal setting
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Connecting generative adversarial networks and actor-critic methods
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Wasserstein generative adversarial networks
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The numerics of gans
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Sarah: a novel method for machine learning problems using stochastic recursive gradient
Nguyen, L. M., Liu, J., Scheinberg, K., and Takáč, M · 2017
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Schmidt, M., Le Roux, N., and Bach, F · 2017
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The mechanics of n-player differentiable games
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On the convergence of single-call stochastic extra-gradient methods
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Training gans with optimism
Daskalakis, C., Ilyas, A., Syrgkanis, V., and Zeng, H · 2018
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A variational inequality perspective on generative adversarial networks
Gidel, G., Berard, H., Vignoud, G., Vincent, P., and Lacoste-Julien, S · 2018
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Stochastic quasi-gradient methods: Variance reduction via Jacobian sketching
Gower, R. M., Richtárik, P., and Bach, F · 2018
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Linear convergence of first order methods for non-strongly convex optimization
Necoara, I., Nesterov, Y., and Glineur, F · 2018
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SGD and hogwild! Convergence without the bounded gradients assumption
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Last-iterate convergence rates for min-max optimization
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Towards closing the gap between the theory and practice of SVRG
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On solving minimax optimization locally: A follow-the-ridge approach
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On the convergence of nesterov’s accelerated gradient method in stochastic settings
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A unified theory of sgd: Variance reduction, sampling, quantization and coordinate descent
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SGD for structured nonconvex functions: Learning rates, minibatching and interpolation
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Revisiting stochastic extragradient
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