| Generalised lipschitz regularisation equals distributional robustness Z Cranko, Z Shi, X Zhang, R Nock, S Kornblith International Conference on Machine Learning, 2178-2188, 2021 | 26 | 2021 |
| Monge blunts Bayes: Hardness results for adversarial training Z Cranko, A Menon, R Nock, CS Ong, Z Shi, C Walder International Conference on Machine Learning, 1406-1415, 2019 | 20 | 2019 |
| f-GANs in an information geometric nutshell R Nock, Z Cranko, AK Menon, L Qu, RC Williamson Advances in Neural Information Processing Systems 30, 2017 | 20 | 2017 |
| Lipschitz networks and distributional robustness Z Cranko, S Kornblith, Z Shi, R Nock arXiv preprint arXiv:1809.01129, 2018 | 14 | 2018 |
| Boosted density estimation remastered Z Cranko, R Nock International Conference on Machine Learning, 1416-1425, 2019 | 12 | 2019 |
| Local differential privacy for sampling H Husain, B Balle, Z Cranko, R Nock International Conference on Artificial Intelligence and Statistics, 3404-3413, 2020 | 10 | 2020 |
| Information processing equalities and the information–risk bridge RC Williamson, Z Cranko Journal of Machine Learning Research 25 (103), 1-53, 2024 | 5 | 2024 |
| The geometry and calculus of losses RC Williamson, Z Cranko Journal of Machine Learning Research 24 (342), 1-72, 2023 | 4 | 2023 |
| An analytic approach to the structure and composition of General Learning Problems Z Cranko PQDT-Global, 2021 | 2 | 2021 |
| The Geometry of Adversarial Subspaces DM Paiton, D Schultheiss, M Kuemmerer, Z Cranko, M Bethge | | 2021 |
| Certifying Distributional Robustness using Lipschitz Regularisation Z Cranko, Z Shi, X Zhang, S Kornblith, R Nock | | 2019 |
| Proper-Composite Loss Functions in Arbitrary Dimensions Z Cranko, RC Williamson, R Nock arXiv preprint arXiv:1902.06881, 2019 | | 2019 |
| Integral Privacy for Sampling H Husain, Z Cranko, R Nock arXiv preprint arXiv:1806.04819, 2018 | | 2018 |
| Integral Privacy for Density Estimation with Approximation Guarantees. H Husain, Z Cranko, R Nock CoRR, 2018 | | 2018 |
| Monge blunts Bayes: Hardness Results for Adversarial Training AK Menon, CS Ong, Z Cranko, R Nock, Z Shi, C Walder | | |
| Monge blunts Bayes: Hardness Results for Adversarial Training—Supplementary Material— Z Cranko, AK Menon, R Nock, CS Ong, Z Shi, C Walder | | |
Richard NockGoogle ResearchVerified email at uniagro.fr
