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Found 11 papers, 2 papers with code
The Geometry and Calculus of Losses
In this paper we systematically develop the theory of loss functions for such problems from a novel perspective whose basic ingredients are convex sets with a particular structure.
Information Processing Equalities and the Information-Risk Bridge
We introduce two new classes of measures of information for statistical experiments which generalise and subsume $\phi$-divergences, integral probability metrics, $\mathfrak{N}$-distances (MMD), and $(f,\Gamma)$ divergences between two or more distributions.
The Geometry of Adversarial Subspaces
We undertake analysis to characterize the geometry of the boundary, which is more curved within the adversarial subspace than within a random subspace of equal dimensionality.
Generalised Lipschitz Regularisation Equals Distributional Robustness
The problem of adversarial examples has highlighted the need for a theory of regularisation that is general enough to apply to exotic function classes, such as universal approximators.
Certifying Distributional Robustness using Lipschitz Regularisation
Distributional robust risk (DRR) minimisation has arisen as a flexible and effective framework for machine learning.
Proper-Composite Loss Functions in Arbitrary Dimensions
The study of a machine learning problem is in many ways is difficult to separate from the study of the loss function being used.
Lipschitz Networks and Distributional Robustness
Robust risk minimisation has several advantages: it has been studied with regards to improving the generalisation properties of models and robustness to adversarial perturbation.
Integral Privacy for Sampling
Privacy enforces an information theoretic barrier on approximation, and we show how to reach this barrier with guarantees on the approximation of the target non private density.
Monge blunts Bayes: Hardness Results for Adversarial Training
A key feature of our result is that it holds for all proper losses, and for a popular subset of these, the optimisation of this central measure appears to be independent of the loss.
Boosted Density Estimation Remastered
There has recently been a steady increase in the number iterative approaches to density estimation.
f-GANs in an Information Geometric Nutshell
In this paper, we unveil a broad class of distributions for which such convergence happens --- namely, deformed exponential families, a wide superset of exponential families --- and show tight connections with the three other key GAN parameters: loss, game and architecture.
