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Found 10 papers, 7 papers with code
Stochastic Gradient Descent-Ascent: Unified Theory and New Efficient Methods
Although variants of the new methods are known for solving minimization problems, they were never considered or analyzed for solving min-max problems and VIPs.
Stochastic Extragradient: General Analysis and Improved Rates
The Stochastic Extragradient (SEG) method is one of the most popular algorithms for solving min-max optimization and variational inequalities problems (VIP) appearing in various machine learning tasks.
A Distributional Robustness Perspective on Adversarial Training with the $\infty$-Wasserstein Distance
We show a formal connection between our formulation and optimal transport by relaxing AT into DRO problem with an $\infty$-Wasserstein constraint.
Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity
Two of the most prominent algorithms for solving unconstrained smooth games are the classical stochastic gradient descent-ascent (SGDA) and the recently introduced stochastic consensus optimization (SCO) [Mescheder et al., 2017].
Online Adversarial Attacks
Adversarial attacks expose important vulnerabilities of deep learning models, yet little attention has been paid to settings where data arrives as a stream.
Stochastic Hamiltonian Gradient Methods for Smooth Games
The success of adversarial formulations in machine learning has brought renewed motivation for smooth games.
Adversarial Example Games
We introduce Adversarial Example Games (AEG), a framework that models the crafting of adversarial examples as a min-max game between a generator of attacks and a classifier.
A Closer Look at the Optimization Landscapes of Generative Adversarial Networks
Generative adversarial networks have been very successful in generative modeling, however they remain relatively challenging to train compared to standard deep neural networks.
A Variational Inequality Perspective on Generative Adversarial Networks
Generative adversarial networks (GANs) form a generative modeling approach known for producing appealing samples, but they are notably difficult to train.
Parametric Adversarial Divergences are Good Losses for Generative Modeling
Parametric adversarial divergences, which are a generalization of the losses used to train generative adversarial networks (GANs), have often been described as being approximations of their nonparametric counterparts, such as the Jensen-Shannon divergence, which can be derived under the so-called optimal discriminator assumption.
