Zakhar Shumaylov

According to our database, Zakhar Shumaylov authored at least 9 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

AI models collapse when trained on recursively generated data.

Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Nicolas Papernot, Ross J. Anderson, Yarin Gal

Nat., July, 2024

Lie Algebra Canonicalization: Equivariant Neural Operators under arbitrary Lie Groups.

Zakhar Shumaylov, Peter Zaika, James Rowbottom, Ferdia Sherry, Melanie Weber, Carola-Bibiane Schönlieb

CoRR, 2024

Score-based pullback Riemannian geometry.

Willem Diepeveen, Georgios Batzolis, Zakhar Shumaylov, Carola-Bibiane Schönlieb

CoRR, 2024

Weakly Convex Regularisers for Inverse Problems: Convergence of Critical Points and Primal-Dual Optimisation.

Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Data-Driven Convex Regularizers for Inverse Problems.

Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola B. Schönlieb

Proceedings of the IEEE International Conference on Acoustics, 2024

2023

Provably Convergent Data-Driven Convex-Nonconvex Regularization.

Zakhar Shumaylov, Jeremy Budd, Subhadip Mukherjee, Carola-Bibiane Schönlieb

CoRR, 2023

The Curse of Recursion: Training on Generated Data Makes Models Forget.

Ilia Shumailov, Zakhar Shumaylov, Yiren Zhao, Yarin Gal, Nicolas Papernot, Ross J. Anderson

CoRR, 2023

2021

Manipulating SGD with Data Ordering Attacks.

Ilia Shumailov, Zakhar Shumaylov, Dmitry Kazhdan, Yiren Zhao, Nicolas Papernot, Murat A. Erdogdu, Ross J. Anderson

Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021

2020

Learned convex regularizers for inverse problems.

Subhadip Mukherjee, Sören Dittmer, Zakhar Shumaylov, Sebastian Lunz, Ozan Öktem, Carola-Bibiane Schönlieb

CoRR, 2020