| Certified robustness of graph convolution networks for graph classification under topological attacks H Jin, Z Shi, VJSA Peruri, X Zhang Advances in Neural Information Processing Systems 33, 8463-8474, 2020 | 50 | 2020 |
| Bregman divergence for stochastic variance reduction: saddle-point and adversarial prediction Z Shi, X Zhang, Y Yu Advances in Neural Information Processing Systems 30, 2017 | 30 | 2017 |
| Generalised lipschitz regularisation equals distributional robustness Z Cranko, Z Shi, X Zhang, R Nock, S Kornblith International Conference on Machine Learning, 2178-2188, 2021 | 29 | 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 | 21 | 2019 |
| Lipschitz Networks and Distributional Robustness Z Cranko, S Kornblith, Z Shi, R Nock arXiv preprint arXiv:1809.01129, 2018 | 15 | 2018 |
| Inductive Two-layer Modeling with Parametric Bregman Transfer V Ganapathiraman, Z Shi, X Zhang, Y Yu International Conference on Machine Learning, 1636-1645, 2018 | 7 | 2018 |
| Distributionally Robust Structure Learning for Discrete Pairwise Markov Networks Y Li, Z Shi, X Zhang, B Ziebart International Conference on Artificial Intelligence and Statistics, 8997-9016, 2022 | 1 | 2022 |
| Distributionally Adversarial Learning Z Shi University of Illinois at Chicago, 2021 | | 2021 |
| Continual Poisoning of Generative Models to Promote Catastrophic Forgetting S Kang, Z Shi, X Zhang NeurIPS ML Safety Workshop, 0 | | |
