Publications associées à Collaborative Sampling in Generative Adversarial Networks

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Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Serge Vaudenay

The maximal achievable advantage of a (computationally unbounded) distinguisher to determine whether a source

Z

is distributed according to distribution

P_0

P_1

, when given access to one sample of

Z

, is characterized by the statistical distance ...

Springer, Cham2020

Collaborative Sampling in Generative Adversarial Networks

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Center-aware Adversarial Augmentation for Single Domain Generalization

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Adversarial Parametric Pose Prior

On the Double Descent of Random Features Models Trained with SGD

Dalton: Learned Partitioning for Distributed Data Streams

Is there an analog of Nesterov acceleration for gradient-based MCMC?

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

System and methods for differential imaging using a lock-in camera

A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

Center-aware Adversarial Augmentation for Single Domain Generalization

System and methods for differential imaging using a lock-in camera

Sublinear Bounds on the Distinguishing Advantage for Multiple Samples

A statistical approach for the automatic identification of the start of the chain of events leading to the disruptions at JET

Locally Differentially-Private Randomized Response for Discrete Distribution Learning

Adversarial Parametric Pose Prior

On the Double Descent of Random Features Models Trained with SGD

Dalton: Learned Partitioning for Distributed Data Streams

A Study on Gradient-based Meta-learning for Robust Deep Digital Twins

Is there an analog of Nesterov acceleration for gradient-based MCMC?