Bobak T. Kiani

According to our database, Bobak T. Kiani authored at least 24 papers between 2018 and 2026.

Collaborative distances:

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

2026

SYK thermal expectations are classically easy at any temperature.

Alexander Zlokapa, Bobak T. Kiani

CoRR, February, 2026

Optimizing Sparse SYK.

Matthew Ding, Robbie King, Bobak T. Kiani, Eric R. Anschuetz

Quantum, 2026

2024

Combinatorial NLTS From the Overlap Gap Property.

Eric R. Anschuetz, David Gamarnik, Bobak T. Kiani

Quantum, 2024

Hardness of Learning Neural Networks under the Manifold Hypothesis.

Bobak T. Kiani, Jason Wang, Melanie Weber

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

Unitary Convolutions for Learning on Graphs and Groups.

Bobak T. Kiani, Lukas Fesser, Melanie Weber

Proceedings of the Advances in Neural Information Processing Systems 37: Annual Conference on Neural Information Processing Systems 2024, 2024

On the hardness of learning under symmetries.

Bobak T. Kiani, Thien Le, Hannah Lawrence, Stefanie Jegelka, Melanie Weber

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Efficient classical algorithms for simulating symmetric quantum systems.

Eric R. Anschuetz, Andreas Bauer, Bobak Toussi Kiani, Seth Lloyd

Quantum, November, 2023

Horizons of Artificial Intelligence in Quantum Computation

Bobak T. Kiani

PhD thesis, 2023

Neural Networks for Programming Quantum Annealers.

Samuel Bosch, Bobak Toussi Kiani, Rui Yang, Adrian Lupascu, Seth Lloyd

CoRR, 2023

Self-Supervised Learning with Lie Symmetries for Partial Differential Equations.

Grégoire Mialon, Quentin Garrido, Hannah Lawrence, Danyal Rehman, Yann LeCun, Bobak T. Kiani

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Equivariant Polynomials for Graph Neural Networks.

Omri Puny, Derek Lim, Bobak Toussi Kiani, Haggai Maron, Yaron Lipman

Proceedings of the International Conference on Machine Learning, 2023

The SSL Interplay: Augmentations, Inductive Bias, and Generalization.

Vivien Cabannes, Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Alberto Bietti

Proceedings of the International Conference on Machine Learning, 2023

2022

Block-encoding dense and full-rank kernels using hierarchical matrices: applications in quantum numerical linear algebra.

Quynh T. Nguyen, Bobak T. Kiani, Seth Lloyd

Quantum, September, 2022

Joint Embedding Self-Supervised Learning in the Kernel Regime.

Bobak Toussi Kiani, Randall Balestriero, Yubei Chen, Seth Lloyd, Yann LeCun

CoRR, 2022

Quantum algorithm for dense kernel matrices using hierarchical splitting.

Quynh The Nguyen, Bobak Toussi Kiani, Seth Lloyd

CoRR, 2022

projUNN: efficient method for training deep networks with unitary matrices.

Bobak Toussi Kiani, Randall Balestriero, Yann LeCun, Seth Lloyd

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Implicit Bias of Linear Equivariant Networks.

Hannah Lawrence, Bobak Toussi Kiani, Kristian G. Georgiev, Andrew K. Dienes

Proceedings of the International Conference on Machine Learning, 2022

2021

Quantum algorithms for group convolution, cross-correlation, and equivariant transformations.

Grecia Castelazo, Quynh The Nguyen, Giacomo De Palma, Dirk R. Englund, Seth Lloyd, Bobak Toussi Kiani

CoRR, 2021

Quantum Earth Mover's Distance: A New Approach to Learning Quantum Data.

Bobak Toussi Kiani, Giacomo De Palma, Milad Marvian, Zi-Wen Liu, Seth Lloyd

CoRR, 2021

Adversarial Robustness Guarantees for Random Deep Neural Networks.

Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd

Proceedings of the 38th International Conference on Machine Learning, 2021

2020

Quantum advantage for differential equation analysis.

Bobak Toussi Kiani, Giacomo De Palma, Dirk R. Englund, William M. Kaminsky, Milad Marvian, Seth Lloyd

CoRR, 2020

Learning Unitaries by Gradient Descent.

Bobak Toussi Kiani, Seth Lloyd, Reevu Maity

CoRR, 2020

2019

Random deep neural networks are biased towards simple functions.

Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Deep neural networks are biased towards simple functions.

Giacomo De Palma, Bobak Toussi Kiani, Seth Lloyd

CoRR, 2018