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Found 5 papers, 3 papers with code
ROBUST ESTIMATION VIA GENERATIVE ADVERSARIAL NETWORKS
In particular, we show that a JS-GAN that uses a neural network discriminator with at least one hidden layer is able to achieve the minimax rate of robust mean estimation under Huber's $\epsilon$-contamination model.
ON BREIMAN’S DILEMMA IN NEURAL NETWORKS: SUCCESS AND FAILURE OF NORMALIZED MARGINS
A belief persists long in machine learning that enlargement of margins over training data accounts for the resistance of models to overfitting by increasing the robustness.
Generative Adversarial Nets for Robust Scatter Estimation: A Proper Scoring Rule Perspective
Robust scatter estimation is a fundamental task in statistics.
Rethinking Breiman's Dilemma in Neural Networks: Phase Transitions of Margin Dynamics
In this paper, we revisit Breiman's dilemma in deep neural networks with recently proposed spectrally normalized margins, from a novel perspective based on phase transitions of normalized margin distributions in training dynamics.
Robust Estimation and Generative Adversarial Nets
Similar to the derivation of $f$-GANs, we show that these depth functions that lead to statistically optimal robust estimators can all be viewed as variational lower bounds of the total variation distance in the framework of $f$-Learning.
