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GAT-GMM: Generative Adversarial Training for Gaussian Mixture Models
Generative adversarial networks (GANs) learn the distribution of observe...
06/18/2020 ∙ by Farzan Farnia, et al. ∙
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Robust Federated Learning: The Case of Affine Distribution Shifts
Federated learning is a distributed paradigm that aims at training model...
06/16/2020 ∙ by Amirhossein Reisizadeh, et al. ∙
9
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GANs May Have No Nash Equilibria
Generative adversarial networks (GANs) represent a zero-sum game between...
02/21/2020 ∙ by Farzan Farnia, et al. ∙
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Generalizable Adversarial Training via Spectral Normalization
Deep neural networks (DNNs) have set benchmarks on a wide array of super...
11/19/2018 ∙ by Farzan Farnia, et al. ∙
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A Convex Duality Framework for GANs
Generative adversarial network (GAN) is a minimax game between a generat...
10/28/2018 ∙ by Farzan Farnia, et al. ∙
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A Minimax Approach to Supervised Learning
Given a task of predicting Y from X, a loss function L, and a set of pro...
06/07/2016 ∙ by Farzan Farnia, et al. ∙
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