f-GANs in an Information Geometric Nutshell. - Richard Nock; Zac Cranko; Aditya Krishna Menon; Lizhen Qu; Robert C. Williamson (NIPS, 2017)

Monge beats Bayes: Hardness Results for Adversarial Training. - Zac Cranko; Aditya Krishna Menon; Richard Nock; Cheng Soon Ong; Zhan Shi; Christian J. Walder (CoRR, 2018)

Integral Privacy for Density Estimation with Approximation Guarantees. - Hisham Husain; Zac Cranko; Richard Nock (CoRR, 2018)

Lipschitz Networks and Distributional Robustness. - Zac Cranko; Simon Kornblith; Zhan Shi; Richard Nock (CoRR, 2018)

Monge blunts Bayes: Hardness Results for Adversarial Training. - Zac Cranko; Aditya Krishna Menon; Richard Nock; Cheng Soon Ong; Zhan Shi; Christian J. Walder (ICML, 2019)

Boosted Density Estimation Remastered. - Zac Cranko; Richard Nock (ICML, 2019)

Local Differential Privacy for Sampling. - Hisham Husain; Borja Balle; Zac Cranko; Richard Nock (AISTATS, 2020)

Generalised Lipschitz Regularisation Equals Distributional Robustness. - Zac Cranko; Zhan Shi; Xinhua Zhang; Richard Nock; Simon Kornblith (ICML, 2021)

The Geometry and Calculus of Losses. - Robert C. Williamson; Zac Cranko (J. Mach. Learn. Res., 2023)

Information Processing Equalities and the Information-Risk Bridge. - Robert C. Williamson; Zac Cranko (J. Mach. Learn. Res., 2024)