The curse of recursion: Training on generated data makes models forget I Shumailov, Z Shumaylov, Y Zhao, Y Gal, N Papernot, R Anderson arXiv preprint arXiv:2305.17493, 2023 | 158* | 2023 |
Manipulating sgd with data ordering attacks I Shumailov, Z Shumaylov, D Kazhdan, Y Zhao, N Papernot, MA Erdogdu, ... Advances in Neural Information Processing Systems 34, 18021-18032, 2021 | 71 | 2021 |
Data-Driven Convex Regularizers for Inverse Problems S Mukherjee, S Dittmer, Z Shumaylov, S Lunz, O Öktem, CB Schönlieb ICASSP 2024-2024 IEEE International Conference on Acoustics, Speech and …, 2024 | 64* | 2024 |
Primordial power spectra from -inflation with curvature Z Shumaylov, W Handley Physical Review D 105 (12), 123532, 2022 | 7 | 2022 |
Quantum initial conditions for curved inflating universes MI Letey, Z Shumaylov, FJ Agocs, WJ Handley, MP Hobson, AN Lasenby arXiv preprint arXiv:2211.17248, 2022 | 4 | 2022 |
Provably Convergent Data-Driven Convex-Nonconvex Regularization Z Shumaylov, J Budd, S Mukherjee, CB Schönlieb NeurIPS 2023 Workshop on Deep Learning and Inverse Problems., 2023 | 2 | 2023 |
Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation Z Shumaylov, J Budd, S Mukherjee, CB Schönlieb arXiv preprint arXiv:2402.01052, 2024 | | 2024 |