10 citations · 41 across the 9 of their papers we have counts for
4 papers · 1 filter
Lipschitz Networks and Distributional Robustness
Zac Cranko, Simon Kornblith, Zhan Shi +1
Robust risk minimisation has several advantages: it has been studied with regards to improving the generalisation properties of models and robustness to adversarial perturbation. W…
Integral Privacy for Sampling
Hisham Husain, Zac Cranko, Richard Nock
Differential privacy is a leading protection setting, focused by design on individual privacy. Many applications, in medical / pharmaceutical domains or social networks, rather pos…
Monge blunts Bayes: Hardness Results for Adversarial Training
Zac Cranko, Aditya Krishna Menon, Richard Nock +3
The last few years have seen a staggering number of empirical studies of the robustness of neural networks in a model of adversarial perturbations of their inputs. Most rely on an…
Boosted Density Estimation Remastered
Zac Cranko, Richard Nock
There has recently been a steady increase in the number iterative approaches to density estimation. However, an accompanying burst of formal convergence guarantees has not followed…